Analyzing Social Media Momentum
India’s 2011-12 Anticorruption Movement
Prepared for
U.S. Government Office of South Asia Policy
By
Sasha Bong
Kenneth Chung
Karen Parkinson
Andrew Peppard
Justin Rabbach
Nicole Thiher
Workshop in International Public Affairs
Spring 2012
updated on page 4 on January 3, 2013
©2012 Board of Regents of the University of Wisconsin System
All rights reserved.
For additional copies:
Publications Office
La Follette School of Public Affairs
1225 Observatory Drive, Madison, WI 53706
www.lafollette.wisc.edu/publications/workshops.html
publications@lafollette.wisc.edu
The Robert M. La Follette School of Public Affairs is a teaching
and research department of the University of Wisconsin–Madison.
The school takes no stand on policy issues; opinions expressed in these pages
reflect the views of the authors.
Table of Contents
List of Tables and Figures .................................................................................... v
Foreword .............................................................................................................. vii
Acknowledgments .............................................................................................. viii
Executive Summary ............................................................................................. ix
1. Introduction ....................................................................................................... 1
2. Background ....................................................................................................... 1
2.A. Basic Theory of Social Networking ............................................................ 2
2.B. Social Media’s Emerging Role in Social Activism..................................... 2
2.C. Corruption in India and the Lokpal ............................................................. 3
2.D. India’s 2011 Anticorruption Movement and Anna Hazare......................... 4
2.E. The Role of Social Media in India’s Anticorruption Movement ................ 6
3. Facebook Data ................................................................................................... 8
3.A. Facebook Usage Terminology and Information Limitations ...................... 8
3.B. Descriptive Statistics of Selected Facebook Pages ..................................... 9
3.C. Social and News Media Volume Comparison .......................................... 10
4. Social and News Media Comparisons ........................................................... 11
4.A. March 25 – April 4: Why the Sub-Committee Meeting Failed ................ 12
4.B. December 22 –January 5: The Movement Changes Direction ................. 14
4.C. Implications for Further Analysis ............................................................. 16
5. Variable Identification and Development..................................................... 16
5.A. Determining Significant Facebook Events ............................................... 17
5.B. Determining Significant Phases ................................................................ 18
5.C. Real-World Event Variables ..................................................................... 19
5.D. Content Themes ........................................................................................ 20
5.E. Day of the Week ........................................................................................ 21
6. Model Specification......................................................................................... 22
6.A. Dependent Variables ................................................................................. 22
6.B. Independent Variables ............................................................................... 23
6.C. Regression Models .................................................................................... 23
7. Model Estimation and Inferences .................................................................. 24
7.A. Regression Model Inferences .................................................................... 27
7.B. Differences Between Natural Log of Likes and Natural Log of Comments
Regression Results .................................................................................... 28
8. Summary and Recommendations for Further Work .................................. 29
Appendix A: Narrative Timeline ....................................................................... 33
Appendix B: Gathering Facebook Data............................................................ 36
Step-by-Step Instructions for Data Collection .................................................. 36
Summarizing Facebook Data in Excel with Visual Basic Macros ................... 37
Appendix C: Facebook Mechanics .................................................................... 39
Terms ................................................................................................................ 40
Appendix D: News Media Data Collection ....................................................... 43
Case Study of Problems with this Data Collection Method: December 5 –
December 9 ............................................................................................... 43
Appendix E: Codebook....................................................................................... 46
Identifiers .......................................................................................................... 46
Activity Variables ............................................................................................. 47
Theme Variables ............................................................................................... 47
Content Variables ............................................................................................. 48
Phase Variables ................................................................................................. 48
Corruption/Phase Interaction Variables ............................................................ 49
Lokpal/Phase Interaction Variables .................................................................. 49
Hazare/Phase Interaction Variables .................................................................. 50
Hunger Strike/Phase Interaction Variables ....................................................... 51
Demonstration/Phase Interaction Variables...................................................... 51
Government/Phase Interaction Variables ......................................................... 52
Real-World Event Variables ............................................................................. 53
Appendix F: Summary Statistics ....................................................................... 54
Appendix G: Predictive Regression Model ...................................................... 57
Appendix H: Regression Results ....................................................................... 58
Appendix I: Diagnostic Tests and Limitations of the Models ......................... 61
Appendix J: Methodological Ideals and Realities ............................................ 62
Privacy Concerns .............................................................................................. 62
Cell Phones ....................................................................................................... 62
Twitter ............................................................................................................... 62
Computer System Requirements ...................................................................... 63
Demographic Information................................................................................. 63
Language ........................................................................................................... 63
Switch to Timeline ............................................................................................ 63
Appendix K: Alternative Means and Methodologies ...................................... 65
Ethical Considerations ...................................................................................... 65
Technical and Budgetary Limitations ............................................................... 66
Making Friends ................................................................................................. 66
Alternative Data Sources .................................................................................. 66
Analytical Toolkits ........................................................................................... 67
Works Cited ......................................................................................................... 73
List of Tables and Figures
Figure 1. Indian Facebook Users by Age Group .................................................... 7
Figure 2. Facebook Activity per Day: April 2011 – February 2012 ..................... 10
Figure 3. Social and News Media Volume, April 2011 – February 2012 ............ 11
Figure 4. Facebook Activity Threshold, 2011-2012 ............................................. 17
Table 1. Significant Events of the Anticorruption Movement, all 2011 ............... 18
Table 2. Phases of the Anticorruption Movement, 2011-2012 ............................. 18
Figure 5. Facebook Activity and Parliamentary Sessions .................................... 20
Figure 6. Change in Themes by Phase on AH and IAC Pages ............................. 21
Figure 7. Facebook AH, IAC Day-of-the-Week Activity..................................... 22
Table 3. Basic Model Summary Results,
Statistically Significant Variables of Main Interest ...................................... 25
Table 4. Interaction Model Summary Results,
Statistically Significant Variables of Main Interest ...................................... 26
Table D-1. Volume Comparison Between News Media
and Facebook Activity .................................................................................. 44
Table D-2. Comparison of News Media Keywords.............................................. 45
Table F-1. Identifiers ............................................................................................ 54
Table F-2. Activity Variables ............................................................................... 54
Table F-3. Theme Variables ................................................................................. 54
Table F-4. Content Variables ................................................................................ 54
Table F-5. Phase Variables ................................................................................... 55
Table F-6. Corruption/Phase Interaction Variables .............................................. 55
Table F-7. Lokpal/Phase Interaction Variables .................................................... 55
Table F-8. Hazare/Phase Interaction Variables .................................................... 55
Table F-9. Hunger Strike/Phase Interaction Variables ......................................... 56
Table F-10. Demonstration/Phase Interaction Variables ...................................... 56
Table F-11. Government/Phase Interaction Variables .......................................... 56
Table F-12. Real-World Event Variables ............................................................. 56
Figure G-1. Idealized Predictive and Responsive Temporal Correlations
in Ln Likes, Relative to Government Action ................................................ 57
v
vi
Foreword
The La Follette School of Public Affairs at the University of Wisconsin–Madison
offers a two-year graduate program leading to a Master of Public Affairs or a
Master of International Public Affairs degree. In both programs, students
develop analytic tools with which to assess policy responses to issues, evaluate
implications of policies for efficiency and equity, and interpret and present data
relevant to policy considerations.
Students in the Master of International Public Affairs program produced this
report for the U.S. government’s Office of South Asia Policy. The students are
enrolled in the Workshop in International Public Affairs, the capstone course
in their graduate program. The workshop challenges the students to improve their
analytical skills by applying them to an issue with a substantial international
component and to contribute useful knowledge and recommendations to their
client. It provides them with practical experience applying the tools of analysis
acquired during three semesters of prior coursework to actual problems clients
face in the public, non-governmental, and private sectors. Students work in teams
to produce carefully crafted policy reports that meet high professional standards.
The reports are research-based, analytical, evaluative, and (where relevant)
prescriptive responses for real-world clients. This culminating experience is the
ideal equivalent of the thesis for the La Follette School degrees in public affairs.
While the acquisition of a set of analytical skills is important, it is no substitute
for learning by doing.
The opinions and judgments presented in the report do not represent the views,
official or unofficial, of the La Follette School or of the client for which the report
was prepared.
Melanie Frances Manion
Professor of Public Affairs and Political Science
May 2012
vii
Acknowledgments
Throughout this project, we benefited from the support of a few individuals.
Dr. Melanie Manion, our advisor for this project, provided us with invaluable
advice and guidance. The La Follette School of Public Affairs Publication
Director, Karen Faster, assisted with meticulous editing of our work. We are
grateful to Professors David Weimer and Jon Pevehouse for their help developing
statistical models. Finally, we would like to thank Keith and the U.S. government
Office of South Asia Policy for the opportunity to work on this project.
viii
Executive Summary
Social media sites such as Facebook are continuously expanding the number of
connections among individuals and groups around the world. Organizers of social
movements are taking advantage of these tools to spread their message and garner
support. To look at the role of social media in social movements, this case study
analyzes Facebook activity related to India’s anticorruption movement in 2011
and early 2012. We examine how the anticorruption movement used Facebook,
how frequency of user activity correlated to the type of content being shared on
Facebook, and ways in which real-world protest events and government actions
affected user activity. We find strong correlations between important real-world
protest events and substantial increases in user activity on Facebook. Using
Facebook activity as a measure of social engagement, we offer conclusions and
implications for relevant actors as they seek to monitor and manage the flow
of social movements.
We began our analysis by systematically gathering and electronically coding the
content of 8,103 top-level posts created on Facebook pages for Anna Hazare and
India Against Corruption from February 2011 through February 2012. Top-level
Facebook posts provide access to direct messages from movement organizers
to supporters. To evaluate the response to these individual posts, we collected
information on the number of likes and comments on a given post as a measure
of user activity. We observe large increases in user activity at the same time as
significant protest events or government action related to the anticorruption
movement. We believe this relationship allows us to treat the level of user
activity as a measure of social engagement with the anticorruption movement
at any given time in our sample period.
We also believe this relationship reveals Facebook as a strong source of
information that can provide insight into the themes that resonate most with
supporters as the anticorruption movement looked to increase social engagement
and on-the-ground participation. We analyzed thematic content of Facebook posts
and used multivariate statistical models to determine how and how much the
content influenced user activity. We developed variables and coded posts for
significant themes present on Facebook. We ran regressions using these themes
as our independent variables or main interest and user activity (measured by
natural logs of likes and comments on Facebook posts) as our dependent
variables. We ran several versions of these regressions, including additional
independent variables for post content, day of the week of post creation, and
occurrence of real-world protests or government action.
We conclude that analysis of Facebook and other social media content can be
useful to relevant actors to a social movement. People can use social media
content to gauge the status of a movement and to identify the goals it seeks to
attain. This content can assist actors outside the social movement, such as
government officials, by offering insight into how a movement’s leaders might
act or react to specific actions or events. Moreover, our analysis identifies specific
ix
themes that resonated most with followers of the anticorruption movement.
Increased use of these themes might have helped garner additional attention,
support, and user activity, with implications for how movement organizers or
outside actors could have increased social engagement with the movement.
We believe a major benefit of this analysis is that it can serve as a starting point
for future analyses of social movements’ use of social media. Continued refinement
of the metrics we developed would allow them to be used to analyze similar social
movements in real time. This implication is key, as relevant actors would want to be
able to react quickly and effectively to steer, repress, or encourage certain aspects
of the movement. This report lays the groundwork for use of the tools necessary for
understanding social media content and changes in user activity. Applications of
these metrics to future movements could provide further insight into the potential
causal linkages between social media content and on-the-ground activity.
Furthermore, as Facebook records and publishes the identity of each user who
likes or comments on a post and links the activity to that user’s profile,
researchers may be able to develop a tracking mechanism to record the frequency
of likes and comments per individual, catalogue and correlate the temporal and
thematic nature of those interactions, and explore the profiles of the most-engaged
or most-influential individuals. In so doing, the researcher could determine the
extent of an individual’s own friend circle. The researcher could learn about an
individual’s stated attendance or role in protest activities and ability to mobilize
friends to attend or participate in another way. Skilled computer programmers and
library science personnel should be able extract this information through the
native Facebook Application Programming Interface and organize it in an
analytically useful way.
x
1. Introduction
Social media, a group of Internet-based applications facilitating the creation and
sharing of messages, pictures, and videos by Internet users, continues to be a
growing phenomenon. The popularity of online profile sites such as Friendster,
Myspace, and SixDegrees has given way to a new wave of social media.
Facebook, initially designed for access by users with college (.edu) e-mail
addresses when launched in 2004, opened to the general public and, as of early
December 2011, had more than 800 million users worldwide.1 Twitter, launched
in March 2006, announced on its fifth anniversary that it hosted more than 140
million tweets a day and more than 1 billion a week.2
Through social media, people are connected to each other around the globe in a
way never experienced before. More than 10 percent of the world’s 7 billion
people are connected by Facebook alone.3 Many more non-Facebook account
holders have profiles on Twitter, Google+, or one of many other global social
media sites.
This report analyzes Facebook activity during India’s 2011-12 anticorruption
movement to determine how social media information can be used to better
understand social engagement through social movements. After the background
section, we begin our analysis by comparing the content of Facebook activity with
the content of articles on web sites of two Indian newspapers in India. We find the
two media types discuss the same events of the anticorruption movement but differ
greatly in tone and language. Given these differences, we conclude that Facebook
activity is a distinct information source to be analyzed. We systematically gathered
and electronically coded 8,103 top-level posts on Facebook pages for Anna Hazare
and India Against Corruption between February 2011 and February 2012. We
theorize that particular features of Facebook posts, such as substantive thematic
content, affected the volume of user activity on the pages during that time. We
develop variables that measure these features and use multivariate statistical
models to estimate their relationships with Facebook activity volume, our
dependent variable. The results of these statistical analyses form the basis of our
conclusions. We discuss the predictive ability of social media in terms of ebbs and
flows of real-world action in social movements. We also discuss ways in which
social media content provides information about the workings of social movements
and the implications for individuals looking to manage, promote, or repress a
similar movement.
2. Background
Social media have not only changed social networking, they provide a valuable
tool for political and social organization and activism. When combined with a
political problem, such as corruption among public officials, social media offer
great potential for social movement participation, useful to the leaders who
organize or emerge from such movements. This section explores the role of social
media in India’s 2011 anticorruption movement.
1
2.A. Basic Theory of Social Networking
The study of social networking, whether online or in person, focuses on social
capital. Social capital is the aggregation of actual and virtual resources an
individual or group attains via networks built from meeting other people or
groups.4 The underlying principle of social capital is that in extending one’s social
network, a person or group then can draw on resources that people or groups in
their network possess or can access. These resources can vary from information to
relationships with other people; from a group’s perspective, resources increase
capacity to organize members across organizations or causes.5
An important component of building an electronic social network, through
Internet sites such as Facebook, is the ability to build two kinds of social capital:
bonding and bridging. Bonding social capital involves a closer relationship among
people, as typically found among family, friends, and close-knit communities.
Bridging social capital is more heterogeneous and slightly more informal than
bonding. Bridging social capital typically involves extending networks to more
diverse people, in terms of personal characteristics or location; associations are
based on one-time meetings or common causes, rather than close, personal
relationships. As approximately 80 percent of social networking users join groups,
opportunities for bridging social capital are likely to grow.6
2.B. Social Media’s Emerging Role in Social Activism
Groups with varying goals and missions have taken advantage of social media
applications to attract and connect with members. Compared to news media,
direct, widespread communications via social media can reach more people across
larger geographic areas and can convey greater amounts of information.7 Online
groups and forums also provide simpler means of organizing because they require
significantly less physical effort to recruit members and distribute information.
Petitions and meeting information can be distributed electronically, rather than
making phone calls or distributing paper materials by mail.8 Groups and
movements increasingly use Internet-based communications to sustain themselves
through member recruitment and fundraising.9 An additional strength of social
media, especially Twitter, is accessibility via cell phones and other handheld
devices, for lightweight, portable communication.10
The types of people social media can reach are important for social movements.
A barrier to social media sites can be Internet access, as it is not universal and it
requires some basic technical skills and resources. These limitations may be less
widespread for some key political groups, such as younger or middle class
citizens. Social media sites are advantageous in this case, as a significant number
of users are young, so these sites serve as conduits for organizations to encourage
coveted youth interest and involvement.
A drawback of social media is that false information and rumors can quickly
spread. The content of posts, when not carefully considered and vetted, can
2
quickly lead to misinformation and controversy. Additionally, increased
awareness and interaction via social media do not always result in real action.
Social media sites allow for easy communication among members, but
communication does not always lead to support and action.11 Finally, in places
where Internet penetration rates are low, social media may not reach large
segments of the population.
Social media can be useful for organizing large numbers of individuals quickly, but
what makes a social media campaign successful is unclear. Some governments have
used censorship and regulation in an attempt to block activists from networking.
In such an environment, social media can be used as organizing tools and open
discussion forums about issues that may influence political change.12 Information
posted on sites can also influence perceptions about another country. As in the
case of the Mavi Marmara in Israel, online social media can be used to garner
international support for policy decisions.13 Social media outlets can help measure
public opinion of government behavior and help anticipate public uprising. In 2011,
social media appear to have played significant roles in organizing and energizing
social change movements, such as the Arab Spring, Occupy Wall Street Movement,
and the 2011-12 anticorruption movement in India.
2.C. Corruption in India and the Lokpal
Political corruption is an ongoing problem in India, acknowledged domestically
and internationally. Transparency International ranked India 95 out of 182 on its
2011 Corruption Perceptions Index, with a score of 3.1. India scored better than
North Korea and Somalia, which had scores of 1. New Zealand ranked No. 1 with
a score of 9.5. Additionally, Transparency International reported that more than
50 percent of Indian respondents disclosed they paid bribes to use basic public
services, which indicates a relatively high level of actual corruption.14 Corruption
can lead to decreased economic development, as theft diverts money for public
services and infrastructure from its original purpose. It can also cause foreign
investors to avoid new investments, as they may wish to avoid paying bribes
levied by corrupt officials. India’s 2005 Right to Information Act called for
increased transparency and required that the government disclose requested
information to Indian citizens, allowing them to expose corrupt acts. However,
this legislation does not directly address corruption, and complaints against
corrupt officials often go unanswered.15
Several highly publicized scams involving the Commonwealth Games, the
Indian Premier League, and the telecommunications industry helped spark
more recent interest in curbing corruption.16 To combat corruption problems,
India’s Parliament has debated establishing an independent commission with
the authority to investigate and punish corrupt government officials. A lokpal
(ombudsman) is a government official or organization that investigates
individual complaints against public officials. However, differences
between the government-drafted Lokpal bill and a version written by
Indian activists caused political tension.
3
Legislators have proposed versions of the Lokpal bill since 1968 but it has not
passed due to disagreements over jurisdiction and coverage of select government
officials. The first Lokpal bill lacked enough support to survive a vote and failed
not only in that introduction, but eight subsequent attempts as well, “none of
which made it to a parliamentary vote.”17 A review in Economic and Political
Weekly stated that the bill was “widely welcomed” at its 1985 introduction in
Parliament. However, this “welcome” was short-lived. Controversy resulted, as
the bill included the Indian prime minister as an investigable government official.
Many critics thought the prime minister should be excluded.18 Continued calls
by Indian citizens for a Lokpal bill have resulted in repeated introductions of
legislation in Parliament. However, as in 1985, controversy in the 2010s over
which public officials should be included under the provision of the new
anticorruption body produced debates that ultimately blocked the bill’s passage.
This controversy is easily understood: the individuals drafting the law are the
people whom the lokpal would scrutinize. Thus the 44-year delay results from
continual debate among ruling parties and individual reluctance to effectively
self-police, given the significant potential gains from corrupt acts.
2.D. India’s 2011 Anticorruption Movement and Anna Hazare
In 2010, following major corruption scandals, the Indian government drafted a
version of a Lokpal bill. Officials convened the Group of Ministers to consider
this government measure for tackling corruption.19 Many citizens and social
activists considered the proposed measure weak, as it did not cover the prime
minister, members of Parliament, and cabinet ministers. Dissatisfaction gave rise
to a national protest movement in 2011. We present a timeline of the 2011 Indian
anticorruption movement’s major events in Appendix A. The face of the
movement has been 74-year-old Anna Hazare of Ralegan Siddhi, a small village
in the state of Maharashtra. Hazare’s fame comes from his work as a community
organizer, particularly his efforts to decrease alcoholism and ensure water access
for individuals in rural areas.20 His tie to national issues came through his work in
2003 advocating for the Freedom of Information Act in India, which eventually
passed in 2005.21 He has led anticorruption protests for two decades. The 2011
anticorruption movement, organized under the name India Against Corruption,
has by far been the largest he has led.
Hazare and others believed that the government’s version of the Lokpal bill was
too weak because the ombudsman it would establish could not investigate actions
of elected officials. The government argued that an ombudsman was a good idea,
but that it would be too powerful if it was able to investigate elected leaders.22
Discussions among Anna Hazare, Prime Minister Manmohan Singh, and a
selection of fellow ministers failed to result in an agreement on a Lokpal bill. In
protest, Hazare began a hunger strike on April 5, 2011, to demand a stronger bill.23
Large protests erupted in support of Hazare. Protesters hoped to pressure the
Indian government into forming a Joint Drafting Committee for a Lokpal bill with
five ministers and five civil society members.24 This protest sparked extensive
4
discussions across news and social media about whether the bill should include
the entire government. It also brought many people to the streets in support of
Hazare’s movement.25 After four days of Hazare fasting, the government agreed
to form the Joint Drafting Committee, and Hazare ended his fast.
The committee met nine times over the course of the next two months. Midway
through this series of meetings, significant differences arose between the
ministers and civil society members. These differences were primarily over whom
the Lokpal would have authority to investigate, the obstacle to earlier versions of
the bill.26 Members were further divided after police arrested social activist Baba
Ramdev and forcefully removed his supporters, who were peacefully protesting
against corruption in Ramlila Maidan (a public square). In protest, the civil
society members of the Joint Drafting Committee boycotted the sixth meeting.27
Differences about the strength of the Lokpal bill remained throughout the rest of
the Joint Drafting Committee meetings, which ended on June 21.28
During a July 3 all-party meeting on the Lokpal bill, the political parties jointly
agreed to bring a “strong and effective Lokpal Bill” to the next session of
Parliament.29 At the end of July, the Lokpal committee completed a second draft,
but it did not include top government officials. After approval from the Cabinet,
legislators brought this version of the Lokpal bill before the Lok Sabha, the lower
house of Parliament on August 4.30 Hazare rejected this version of the bill and
announced a second hunger strike.31 Hazare announced that this second strike
“to the death” would begin on August 16 and only end if the government agreed
to pass a Lokpal bill that included the entire government. In response, the New
Delhi police denied Hazare permission to stage an indefinite fast at the Jantar
Mantar observatory. Police restrictions looked to limit the protest to one day and a
maximum of 2,000 participants.32 At the same time, government officials began a
smear campaign against Hazare that included accusations of corruption, which
were quickly withdrawn.33 Prime Minister Manmohan Singh used his August 15
Independence Day speech to criticize Hazare and discuss the government’s efforts
to pass a strong Lokpal bill.34
After refusing to agree to police instructions for his second hunger strike, Hazare
and key members of India Against Corruption were arrested early in the morning
of August 16, before Hazare started his hunger strike, and taken into “preventive”
custody. Hazare’s supporters released a video that had been prepared in case of
arrest. It called for fasts, peaceful protest, and a jail bharo, where protesters
sought arrest so as to fill India’s prisons. Nearly 600 demonstrations and protests
erupted across the country in response. Instead of Hazare’s arrest slowing the
anticorruption movement, as politicians had hoped it would, the movement
exploded. By the afternoon of August 16, large protests had occurred all over
India, and 1,300 people had been arrested as part of the jail bharo.35
Hazare and his followers were officially released from jail later that day, but
Hazare refused to leave until given permission to fast publicly without policeimposed restrictions.36 The government spent most of the next day negotiating
5
with him. After making a deal with police, Hazare left jail on August 18 and
continued his hunger strike at Ramlila Maidan. Tens of thousands of people
gathered in Ramlila Maidan to show their support and fast with Hazare, while
thousands more supported Hazare online through social media. The way the
government handled the situation angered many people, which left the ruling
party humiliated.37 During the following days, the Indian government reduced its
personal attacks on Hazare and instead suggested that a “foreign hand” was
driving the protest movement. The government also repeatedly asked Hazare to
end his fast.38 After further discussions with Hazare and debates in Parliament,
the government agreed to debate all versions of the Lokpal bill.39 This agreement
prompted Hazare to end his hunger strike on August 28.40
Despite strong support for the movement and its ability to gain several key
concessions from the Indian government, the stronger version of the Lokpal bill
developed in August has not passed. On December 27, 2011, the Lok Sabha,
Parliament’s lower house, approved the government’s latest version of the bill,
which Hazare thought was still too weak. He began another hunger strike in
Mumbai to protest the government moving forward with the weaker Lokpal bill,
and this strike drew a crowd of 10,000 supporters, a marked decrease from
previous rallies. Hazare ended his third hunger strike, claiming health concerns,
shortly after the Lok Sabha passed a weaker version of the Lokpal bill.41 In the
Rajya Sabha, the upper house of India’s Parliament, both parties introduced
more than 180 amendments to this version of the bill during a 13-hour session.
Ultimately, they recessed without a vote.42 Promises were made to again take up
the bill in March 2012 during the next legislative session. In the subsequent
months, Hazare has been in and out of the hospital but has promised to continue
the fight for a strong Lokpal bill when his health improves.43
2.E. The Role of Social Media in India’s Anticorruption Movement
Anticorruption movement organizers and supporters used social media to quickly
broadcast information and organize protests. Indians also used social media to
show support for India Against Corruption (IAC) and Anna Hazare, indicated
on Facebook by “likes” on posts. In the first four days of its existence, IAC had
116,000 fans on its community Facebook page.44 People created many other
Facebook pages, and individual social media users debated, posted statuses,
and uploaded videos and photos throughout the movement. Social media analyst
Gaurav Mishra estimates that the total online support for the movement was
around 1.5 million people.45
Facebook hosts multiple Anna Hazare-related pages in English and Hindi, with
tens of thousands of followers and supporters. The official IAC Facebook page
had more than 500,000 followers as of February 7, 2012.46 Users can follow and
access information about the anticorruption movement through applications for
smart phones and other mobile devices. The IAC smart phone application has as
many as 50,000 users.47 The organization used all these outlets to publish photos
of Anna Hazare fasting, pro-Lokpal rallies, and examples of corruption. During
6
this social media onslaught, Hazare gained support from other prominent
Indian activists, as well as the general populace.
For a social movement to be able to use social media effectively to advance its
cause, large parts of the population must have access to the Internet and people
must be able to use the Internet and social media freely. The non-governmental
research organization Freedom House scores the Internet in India as “mostly
free” in its 2011 Freedom of the Net evaluation.48 India established the Internet
Technology Act in 2000, and a 2008 amendment gave the government authority
to block websites and Internet content, as well as outlaw offensive or inflammatory
content.49 Comments about religion or caste can be particularly volatile in India,
given its history of religion-based riots. Google gives government officials
information about Internet protocol addresses and service providers when
requested.50 Private blogs have had to remove posts upon threat of legal action.51
In the first six months of 2011, the Indian government requested 358 removals
from Google, mostly from Orkut and YouTube, the majority for content
criticizing the government.52
According to the World Bank, 5.3 percent of the total Indian population
had Internet access in 2009.53 With a population of approximately 1.1 billion
people, this usage rate means that more than 58 million Indians use the Internet.54
Fifty-six percent of Internet users use Facebook.55 Of those, 73 percent are men;56
50 percent are 18 to 24 years old,57 as pictured in Figure 1. Facebook posts and
news reports show that the anticorruption movement centered on urban areas.
The Facebook demographics suggest the movement engaged urban men but
left large segments of the population out of the debate.
Figure 1. Indian Facebook Users by Age Group
Percentage of User Base
50%
40%
30%
20%
10%
0%
13-15
16-17
18-24
25-34
35-44
Age Group
Source: Socialbakers.com
7
45-54
55-64
65+
3. Facebook Data
The emergence of social media has changed how people engage with each other
and with social movements. As more people access social media, the greater the
opportunity to measure the extent to which Facebook activity reflects social
engagement in a particular social movement. We determined the themes, events,
and other variables that seemed to drive social media activity throughout the year.
We then identified and analyzed relationships between Facebook activity and
real-world events by using Facebook user activity as a reflection of social
engagement with the movement.
To analyze Facebook activity related to India’s 2011-12 anticorruption
movement, we collected data on all top-level posts from February 2011 through
February 2012 on Facebook pages for Anna Hazare and IAC. We detail how we
collected the data in Appendix B: Gathering Facebook Data. Data on likes,
comments, and shares indicate levels of social engagement in the movement.
Top-level post content can be coded and used to identify the themes present
throughout the movement and how Facebook discussions changed over the span
of our analysis. We also analyzed articles about the movement published in two
online English-language newspapers, The Hindu and The Times of India. We
identified these articles through key word searches on the two news sites. We
sought to determine whether Facebook activity mirrors news media activity in
content and volume. A cross-referencing of Facebook top-level post content
against traditional news stories helped us to distinguish points where Facebook
content provides information different from what readers would encounter by
following only the online newspaper accounts. Our reading of these sources
determined that Facebook content provided a window into the intentions and
workings of the movement, while news media accounts may have a very different
message. The difference in content may be due to movement leaders distributing
one message to news media as they try to win the support of a more neutral
audience, while providing different or more detailed messages to Facebook
users who would be assumed, on whole, to support the movement.
3.A. Facebook Usage Terminology and Information Limitations
The publisher of a Facebook page posts information on his or her page with a
brief amount of text, photos, or links to websites. For our analysis, these are
top-level posts. Responses to these top-level posts are what we describe as user
activity. Three measures of user activity are available on Facebook: “likes,”
comments, and “shares.” We only use likes and comments to measure user
activity; we do not include shares in our definition. We describe these features
briefly below and at length in Appendix C: Facebook Mechanics.
Likes: The level of user interaction required to like a post is a single click,
generating an increment of one additional like for that post. Facebook allows
each user to like each unique post only once. Therefore, the total number of likes
8
for each post represents the total number of unique users who have viewed that
post and clicked the like option.
Comments: Comments involve a deeper level of user interaction and are
independent of liking a top-level post. The act of adding a comment increases the
comments count by one. A user may add an unlimited number of comments per
post. Comments demonstrate that a unique post has been viewed and that the view
the user wishes to share his or her thoughts on the post or on preceding comments
with other users.
Shares: While viewing a post, a user may click the share option that presents the
ability to copy the post, along with an open comment box, to his or her personal
wall, a friend’s wall, or into a private message. Sharing generates a cross-linked,
independent thread in the sharing user’s profile page or the alternative location of
his or her choosing. Facebook added shares as a feature late in our sample period.
No data on shares are available for early in the sample period, and user familiarity
was too low to have confidence in shares as reliable measures of social media
momentum in the short time it was available during the sample period. Thus, we
omitted shares from our analysis.
A limitation of these measures is that users may like or comment on a post at any
time after it has been posted to the page. Thus, our figures reflect the number of
likes and comments on posts created on a specific date, not the number of likes
and comments added on that date. However, as most likes and comments on a
given post do occur soon after posting, they are strong indicators of social
engagement in the movement on a given date.
3.B. Descriptive Statistics of Selected Facebook Pages
We selected two Facebook pages for data collection based on the volume of their
followers: the IAC page (http://www.facebook.com/IndiACor), which boasts
550,000+ visitors who have liked the page, and the Anna Hazare page (AH page)
(http://www.facebook.com/annahazare), which has 415,000+ liking visitors. Our
search revealed no other Facebook pages related to the 2011-12 anticorruption
movement that came close to this volume of followers.
The two pages vary in some characteristics of author and visitor activity. The
number of posts per day differs dramatically: the AH page averages two posts
per day (range 0 – 20) and the IAC page averages 19 (range 3 – 157) during the
time period under analysis. Also divergent are the numbers of likes per post and
comments per post between the two pages. The AH page shows greater activity
per post, averaging 1,201 likes per post (range 6 – 5,615) versus the IAC page’s
613 (range 16 – 1,880). The same pattern holds for comments per post.
The AH and IAC pages often share content. Posts on one page are often crossposted on the other’s page. Post themes follow common patterns across pages.
Theoretically, the IAC page encompasses a broader anticorruption movement,
9
while the AH page would focus more narrowly on the work of Anna Hazare.
However, the period surveyed in this report is almost exclusively focused on
Anna Hazare’s activities. For these reasons, we pooled AH page and IAC page
data when analyzing social media user activity and relevant events.
A high number of posts per day does not necessarily result in a high number
of likes and comments, as seen in Figure 2. We attribute this disparity to “post
overload”: the number of posts in a given day can exceed a user’s interest and
time availability to continuously monitor and participate in the discussion. Prior
to the August protest event, significantly more posts per day were required to
elicit the same number of likes and comments. We believe this variation reflects a
period of “audience building” during which more individual users became aware
of the Facebook pages and began to respond to content. Once this audience had
been built up, a dedicated group of AH and IAC page followers responded
predictably to new page activity. Figure 2 demonstrates spikes in both the number
of comments and likes that occurred at certain points. These spikes in social
media activity occur at the same time as significant real-world events highlighted
in our background section.
Likes, Comments
per Day, Thousands
250
180
160
140
120
100
80
60
40
20
0
200
150
100
50
0
Apr May
Jun
Jul
Aug
Sep
AH, IAC Likes per Day
Oct
Nov Dec
Jan
Posts per Day
Figure 2. Facebook Activity per Day: April 2011 – February 2012
Feb
AH, IAC Comments per Day
AH, IAC Posts per Day
Source: Authors’ calculations
3.C. Social and News Media Volume Comparison
The level of Facebook posts and activity provides one picture of the
anticorruption movement and the type of content being posted to public Facebook
pages. To develop alternative perspectives on the movement, we collected
information about the volume of references to the movement in news media
sources. We used two of the largest online English-language newspapers in India,
The Times of India and The Hindu. We chose these sources, as other select media
outlets do not have online archives that cover our entire sample period, and we
were limited to sampling only English newspapers due to language restrictions.
10
Further limitations and issues with the data are discussed in Appendix D: News
Media Data Collection. Figure 3 demonstrates that the number of daily news
stories referencing the movement mirrors the trend in social media activity
throughout our sample period.
250
300
200
250
200
150
150
100
100
50
50
0
0
Apr May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
News Articles per Day
Facebook Activity per Day,
Thousands
Figure 3. Social and News Media Volume, April 2011 – February 2012
Feb
AH, IAC Likes per Day
AH, IAC Comments per Day
Times of India, The Hindu News Articles per Day
Source: Authors’ calculations
4. Social and News Media Comparisons
A relatively strong correlation exists between volume trends of Facebook
and online versions of newspapers The Times of India and The Hindu, as
demonstrated in Figure 3. Content, however, can be very different across sources.
One source may give a different depiction of an event than the alternate source,
or an event may be referenced in one source and not the other. The two examples
that follow illustrate how an analysis of news media alone misses some
information available through social media. Movement organizers are likely
to recognize that information disseminated in news media is likely to go to a
relatively more diverse audience. This understanding of different audiences may
prompt organizers to put forth a different message or use a different tone across
media. On the IAC and AH pages, the audience comprises mostly individuals who
support the movement. The Facebook messages directed toward supporters may
provide a better window into the true message of the movement, rather than
polished statements tempered for news media. Additional information gained
from social media can help to explain the real-world events of India’s 2011-12
anticorruption movement. Reading content on Facebook reveals insights into
movement plans. Although we completed this analysis after significant social
media activity on the movement subsided, we demonstrate how reading of the
Facebook content in real time could have helped individuals predict a
movement’s likely responses.
11
4.A. March 25 – April 4: Why the Sub-Committee Meeting Failed
As the April 5 start date of Anna Hazare’s threatened hunger strike approached,
the government attempted to persuade IAC to reconsider. Organization members
and a government sub-committee scheduled a meeting for March 28 to discuss an
IAC-written draft of a Lokpal bill. The only way to get Hazare to cancel the fast
and protest was if an agreement could be reached to move forward on passing
IAC’s version of the bill. Specific to this meeting, progress would require the
government agreeing to form a joint drafting committee, a body made up of IAC
members and government officials, to draft official legislation mirroring the IAC
version of the bill. IAC is quoted in the media after the meeting, stating that it had
been “much anticipated.”
Indo-Asian News, March 28
“The much anticipated sub-committee meeting on Lokpal Bill
failed to yield any positive results with the government showing no
intention of forming a joint committee,” IAC said in a statement.
[sic]58
Despite IAC’s apparent anticipation of the meeting, the event received no
coverage on the IAC or AH pages until a March 25 post announced that Hazare
would not attend. This lack of promotion was a sharp contrast to other events
created, supported, or attended by members of IAC that attracted multiple posts
per day as advertisements and reminders.
IAC Facebook post, March 25, 6:51 a.m.
Breaking news from IAC Newswire!!!! Anna Hazare refuses to
attend meeting with sub-committee. [sic]59
A post later that morning stated the movement’s negative feelings regarding
further meetings with the prime minister or other government committees.
IAC Facebook post, March 25, 12:11 p.m.
After meeting with PM ended inconclusively, there is no point in
meeting more ministers and committees – we demand Jan Lokpal
and we will have it!!!! [sic]60
Despite declaring the pointlessness of further meetings, IAC sent a contingent of
members to meet with the government on March 28. Afterward, IAC released a
statement describing the lack of progress on inducing the government to form a
joint drafting committee on which general public members held at least half of the
seats. The release stressed that joint committees had been formed for other
legislation. The government insisted that this type of action had no precedent and
that the sub-committee did not have the power to establish a joint committee.
Following the failed meeting on March 28, the prime minister expressed his
“disappointment” that Hazare would continue with his fast, though he did state
12
that he respected “Hazare and his mission.” The government sought to depict IAC
members as stubborn and unwilling to negotiate what would be included in the bill.
Times of India, April 4
The sub-committee headed by defence minister A.K. Anthony met
colleagues of Hazare “but the interactions proved fruitless as the
activists were insisting on the government accepting their draft in
full,” the PMO said.[sic]61
Conversely the AH page criticized the government.
AH Facebook post, April 4, 12:17 p.m.
Instead of welcoming Anna’s suggestions and try to bring systemic
changes they are disappointed with this man who is willing to risk
his life to fight against this disease – shame on them!!!! [sic]62
Though the March 28 meeting may have been an attempt by the government to
appease IAC, Facebook content leads us to believe that IAC never actually
considered entering the meeting with the intent to negotiate to an agreement that
would have cancelled the fast and protest.
The AH page was created on March 28, the day of the meeting. The first post was
an invitation to the event page for the fast to be held in early April. Additionally,
on the IAC page, information about Hazare’s fast consumed a large percentage
of posts through March, leading up to the meeting. Posts do not indicate a
tempering of plans in regard to Hazare’s fast in the event an agreement was
reached. Language in the posts does not project a feeling of plans for “if,”
but rather a sense of plans for “when.”
IAC Facebook post, March 25, 11:09 a.m.
Delhi / NCR Volunteer Meet on Sunday 27th March – to mobilise
maximum people for Anna Hazare’s fast. Lets create a Tahrir Chowk
at Jantar Mantar. All Delhi volunteers, please join us. [sic]63
This response suggests once the movement had raised enough support and
reached a level of organization for this specific protest, it planned to go ahead
regardless of government response or attempts at appeasement. The movement
taking on this attitude toward this meeting appears indicative of a relative “tipping
point” at which government discussions could not appease the movement. IAC
decided that too much time and energy had been expended to organize and
mobilize; cancelling the event could kill movement momentum. As support grew,
so did the size of the concession needed by the government to cancel the fast.
Monitoring the AH and IAC pages in the days leading up to the meeting would
have revealed that the government was not taking strong enough action to lead to
a cancellation of the protest. We see from Facebook a distinct choice by the
movement to push ahead with preparation for the protest. If only the two
newspapers, The Times of India and The Hindu, had been considered, the picture
13
would be one of the movement and the government each blaming the other for
being inflexible and at fault for the lack of progress in negotiation.
4.B. December 22 –January 5: The Movement Changes Direction
In the days leading up to the debate and vote in Parliament on December 27, the
AH and IAC pages called people to join protests planned for the time of the vote
in response to the “weak version” of the Lokpal bill under consideration.
Numerous posts on the AH and IAC pages asked people to join the jail bharo, and
one post announced that more than 100,000 people had signed up to be arrested
on December 29.
The movement used strong words leading up to the protest:
IAC Facebook post, December 25, 4:54 a.m.
We are not afraid of being monitored. If a strong Lokpal is passed,
we’ll go ahead with our next movement. If the government tries to
pass a weak Lokpal, we’ll oppose it tooth and nail. We’ll oppose
the party in power in center, until a strong Lokpal is passed. [sic]
As Hazare began his fast, turnout was disappointing, though some credit this lack
of participation to rumors that people who participated in the jail bharo would be
penalized with job loss or loss of visas. IAC also worried that the government
may have disrupted communication by limiting cell service.
IAC Facebook post, December 27, 1:01 a.m.
Serious communication issues in Mumbai – Airtel moblice phones
and data cards at MMRDA not working – Govt’s dirty tricks,
sabotage? [sic]
As the bill passed the lower house, Hazare pledged to continue the fast.
AH Facebook post, December 27, 11:49 a.m.
Anna Hazare has fallen sick and is running 102 degrees
temperature. He has refused to end his fast. Deep concern for his
health continues as the team urges him to end his fast. Messages of
immense worry are pouring in.
IAC Facebook post, December 27, 11:37 p.m.
Anna will continue on his resolve to fast for a strong Lokpal!
IAC Facebook post, December 27, 11:45 p.m.
People pressure made Parliament work, debate and pass Lokpal
Bill after 42 years. This is no mean feat but the victory is
incomplete. If Lokpal is to be functional CBI must be brought
under Lokpal and pro-corruption sections must be removed. The
battle is on, Jail Bharo from 30 December will ensure that India
gets a strong anti-corruption law. Get ready!!
14
Despite these strongly worded pledges to continue, IAC posted quotes from
Hazare calling off the fast and jail bharo less than six hours later.
IAC Facebook post, December 28, 4:48 a.m.
“Jail Bharo is suspended for now!” Anna
IAC Facebook post, December 28, 4:49 a.m.
“Due to what is happening in the Parliament, the Anshan [fast] will
finish today and our aim for now will be voter awareness and the
elections in the five states,” Anna.
Organizers called off the fast due to Hazare’s failing health and the passage of a
bill not supported by the movement. The fast drew disappointing numbers and
failed to prevent the government from taking action; unlike its responses in April
and August, the government did not move to appease the movement. This decline
in interest led to an announcement regarding the new direction of the movement.
IAC Facebook post, December 28, 4:28 a.m.
“We have two years till the National elections. We will tour all
over the country till then and spread awareness about the rights of
people and ask them not to vote for the corrupt and criminals.”
Anna
These posts are a stark change in the direction of the movement. The decision
seems to have been made quickly, given the abrupt change in tone and content of
Facebook posts within a six-hour period. This change in direction may be why the
movement seemed to lose momentum from this point forward. The movement
threw away a month of buildup to the fast and jail bharo. It may have changed
focus too quickly however, as organizers did not seem prepared to guide
followers in the next step.
IAC Facebook post, January 5, 2012, 9:21 p.m.
The anti-corruption movement is at the crossroads today. Where do we go
from here? …. If we do not go for the tour of election-bound states, what
should we do? Should Anna go for another fast? But the government has
already indicated that if people participating in the movement don’t
translate into votes, they don’t care. Some have suggested we should form
our own party. But we neither have the will nor the capacity.
The excerpt above demonstrates that the movement broke down in some ways.
It was not using Facebook to advertise specific events or call for volunteers and
supporters to attend planned events. Rather, at this point, the movement looked to
find direction itself. We believe this change in tone allowed for members to divide
as they chose how to move forward. The dilution of support from various groups
prevented the cohesion necessary to move forward. Without events to attend or a
strong leader issuing statements about the way ahead, people lost faith and
interest in the movement. Decrease in user activity on Facebook reflects this
15
disengagement. We do not see another spike in user activity during the remainder
of our sample period.
The previous April, IAC had refused to cancel the fast, given the amount
of time and organization that had gone into it, for fear of killing momentum.
In December, the movement chose the opposite path and canceled the fast and
jail bharo. It was not able to recover momentum following this cancellation. It
appears that a government action strong enough to force the movement to change
its plans was able to kill momentum and quell support of the movement. This shift
could be identified as the “tipping point” at which government action, rather than
appeasement, affected the movement’s ability to move forward.
4.C. Implications for Further Analysis
Cross referencing Facebook posts with articles from The Hindu and The Times of
India illustrates that gaps may exist between social and news media coverage of
India’s 2011-12 anticorruption movement. Social media posts provide additional
information as well as nearly instantaneous insight into changes in plans and
directions of the anticorruption movement. Due to the availability of information,
the number of users, and the amount of interaction these users have with the
movement via social media, we used Facebook posts on the AH and IAC pages to
measure movement action as we developed analytical models. Such use of posts
makes sense given the increases in volume of user activity at the same time that
real-world social engagement is occurring on the ground as a part of the
movement. We used user activity and post content to measure social engagement
and to identify the themes that keep individuals invested in the anticorruption
movement at a given point in our sample period.
Given massive amounts of content, it would be nearly impossible to monitor all
Facebook posts and crosscheck content with newspaper articles in a reasonably
short period of time through simple reading of posts and articles. This reality
drives the need for a tool to more quickly analyze this content.
5. Variable Identification and Development
An initial analysis of user activity across our sample period demonstrated that a
strong correlation existed among the level of user activity, the volume of news
media coverage of the movement, and the occurrence of real-world protests and
action by the movement. This correlation allowed us to use likes and comments
on our selected Facebook pages as a measure the social engagement with the
movement at a given point in time during our sample period. In our analysis we
sought to determine the variables that affected the level of user activity with toplevel posts on Facebook and, based on the existing correlation, gain insight into
how Facebook themes and content influenced social engagement with the
movement. To measure effects on Facebook user activity we identified and
developed variables for significant movement events, significant phases, content
themes in individual posts, and days of the week to be used in analytical models
16
to determine the drivers of social engagement over the span of this movement.
Subsequent parts of Section 5 discuss how we developed these variables for use in
our quantitative analysis. Section 6 describes the regression models we estimated.
Section 7 presents and interprets the results of our analysis.
5.A. Determining Significant Facebook Events
Graphical representation of the data, such as Figure 3, identifies dates during our
sample period when Facebook user interaction on the AH and IAC pages spiked.
Through comparison of the days of these Facebook interaction spikes and our
timeline of real-world activity, we observed that most of these spikes seem to be
closely correlated with real-world events. Therefore, we used spikes in Facebook
activity to identify important events that may warrant further analysis.
To distinguish between increases in daily Facebook interaction due to important
events and more minor events (or noise), we developed a “signal-to-noise”
threshold above which we define an event as “significant.” We set that threshold at
one standard deviation above the mean of the natural log of likes and the natural log
of comments. Additionally, to qualify as a significant event, the threshold must be
crossed in terms of both likes and comments. Analysis by this metric yields nine
significant Facebook events throughout the period of analysis, as seen in Figure 4.
Figure 4. Facebook Activity Threshold, 2011-2012
4
1
3
Ln Likes per Day
11
2
10
5
8
7
6
9
9
10
8
9
7
8
6
Ln Comments per Day
12
5
7
Apr
May
Jun
Jul
Aug
Sep
Ln AH, IAC Likes per Day
Oct
Nov
Dec
Jan
Feb
Ln AH, IAC Comments per Day
Mean + 1 s.d. Threshold
Source: Authors’ calculations
To establish effective date ranges around significant events, we reviewed the
pooled likes and comments data and determined the date at which activity volume
began an upward trend. This pre-peak data “valley” is our event start date. Where
likes and comments differ in start date, we selected the earlier date of the pair.
Event peaks are classified as the date of highest activity. Event end dates are
17
classified as the first day in which likes or comments fall below the level of its
initial activity level, or valley, following the peak. The exception to these
definitions is event 4, which was sustained for a longer date range and had
multiple peaks and dips before dropping below threshold. We defined event 4’s
start date as the first valley below threshold, its peak as the highest point during
its date range as an event, and its end date as the first dip low enough to cross its
initial starting valley. Identified events are listed in Table 1.
Table 1. Significant Events
of the Anticorruption Movement, all 2011
Event
Month
Start Date
Peak Date
End Date
1
Apr
3
8
14
2
Jun
3
8
11
3
Aug
3
4
8
4
Aug
12
16
31
5
Sep
1
2
10
6
Oct
16
18
22
7
Dec
5
6
9
8
Dec
10
11
14
9
Dec
18
27
31
Source: Authors’ calculations
5.B. Determining Significant Phases
To incorporate the passage of time and to differentiate among distinct stages in
the movement, we developed a phase variable based on real-world events. In
general, this variable divides the movement by the three main protests that took
place in April, August, and December and their interim periods. The periods of
increased Facebook user activity around these three protests are phases 2, 4, and
6. The remaining phases, 1, 3, 5, and 7 are the lull periods, or periods of relatively
lower user activity surrounding these protest events during the course of our
sample period. The phases are listed in Table 2.
Table 2. Phases of the
Anticorruption Movement,
2011-2012
Phase
Start Date
End Date
1
Jan 27
Apr 2
2
Apr 3
Apr 13
3
Apr 14
Aug 11
4
Aug 12
Aug 31
5
Sep 1
Dec 20
6
Dec 21
Dec 30
7
Dec 31
Feb 19
Source: Authors’ calculations
18
5.C. Real-World Event Variables
We observed in Figure 3 that spikes in Facebook user activity and actions on the
ground have a strong correlation. Due to this finding, we believed inclusion of
real-world events in the regression models was important. The two types of realworld activities of interest are on-the-ground protests and government actions in
response to (or related to) the movement. These variables help determine the
importance of real-world events in the level of social media engagement.
Organized protest events define the real-world protest event variable. We
identified the dates of protests using our narrative timeline. As protests are usually
planned events, a significant level of Facebook discussion and promotion about
the protest happened on the day before it was to occur, and discussions about
major protests likely continued afterward. As a result, we expanded our protest
period to include a day before and after the organized protests. The result is a
binomial variable coded as one for the dates of protests, including the date before
and after, and zero otherwise.
We divided the real-world government action variable into two categories,
negative and positive government actions from the anticorruption movement’s
perspective. We defined positive government actions as the government trying
to appease the movement or to work toward meeting its demands. These actions
include government officials meeting with protest leaders and with each other
to discuss the Lokpal bill and the government making agreements with Hazare.
Negative government actions are deliberate efforts to repress or unravel the
movement and actions to which the movement strongly objects. These actions
include the arrest of protestors, explicit attempts to discredit Hazare, or action to
move forward with a version of the Lokpal bill that the movement believes is too
weak. We assign each variable a one for each day of government action and the
day after. Each code includes the day after an action to encompass subsequent
Facebook discussion. The day preceding government action was not included
because the dates for many government actions were not known to the public in
advance, making it less likely that significant discussion in anticipation of these
events occurred. All days not fitting this description of government action or
following day are coded as zero.
Last, we included a variable for whether Parliament was in session on a given
day. The Parliament conducted the Budget, Monsoon, and Winter sessions during
our sample period.64 This variable is coded as one on days Parliament was in
session and as zero otherwise. Inclusion of this variable helped to control for the
fact that protests were more likely to be scheduled to coincide with the times
when government was more likely to take action on the Lokpal bill. The relevance
of inclusion is seen in Figure 5, as two of the three largest spikes in user activity
occurred while the parliament was in session.
19
2.5
Budget
Monsoon
Winter
1
2.0
Parliamentary Session
AH, IAC Likes and Comments per Day,
Ten Thousands
Figure 5. Facebook Activity and Parliamentary Sessions
1.5
1.0
0.5
0.0
0
Feb Mar Apr May Jun
AH, IAC Likes
Jul
Aug Sep Oct Nov Dec
AH, IAC Comments
Jan
Feb
Parliamentary Session
Sources: PRSIndia.org and authors’ calculations
5.D. Content Themes
We developed variables to identify and code the themes in each Facebook post in
our sample period. The purpose was to gain insight into the types of information
that drove social media response. Using likes and comments as measures of social
engagement, we believe this process helps to identify issues followers of the
movement considered important and specific themes central to the message of the
movement.
We identified key words by studying the movement and reading a sample of
Facebook posts. We organized these keywords into six major theme categories:
Hazare, corruption, Lokpal, hunger strike, demonstration, and government. A full
specification of the thematic keywords used to sort posts into these six theme
categories (or no category) is presented in Appendix E. At the same time, we
developed five post-content type categories: video, blog, news (traditional), web,
and photo.
The prevalence of the defined themes in the count of Facebook top-level posts
differs across phases. Figure 6 shows the percentage of posts incorporating an
individual theme in each phase. In phase 1, the themes of corruption and
demonstration dominate; in phase 2, the majority of posts are about Hazare as he
gains attention during his hunger strike. During phase 3, the Lokpal bill is the most
common theme in Facebook posts, which is when the Joint Drafting Committee was
meeting to discuss the bill. In phases 4 and 5, posts about Hazare are again the most
common following his arrest and protest, and his continued rise in notoriety as the
face of the movement. In phase 6, both the Lokpal bill and Hazare are important
subjects in the posts, reflecting the bill’s passage in Parliament and Hazare’s ongoing
fast. Phase 7 is primarily about Hazare, mainly discussing his hospitalization.
20
Figure 6. Change in Themes by Phase on AH and IAC Pages
Percentage of Posts
Containing Theme
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
Phase
Corruption
Lokpal
Hazare
Hunger Strike
Demonstration
Government
Source: Authors’ calculations
5.E. Day of the Week
The average number of posts per weekday varies significantly, with the highest
posting activity occurring on Thursday and the lowest on Monday. We depict this
posting schedule in Figure 7. Also represented is the percentage of weekday posts
with a significant number of likes and comments. This designation of “significant”
reflects post engagement, indicating those posts that have accumulated a total
number of likes and comments equal to at least the sample period average plus
one standard deviation.
Upon cursory review, we see that posts on Tuesday and Wednesday generate a
greater percentage of posts with significant likes and comments than other days.
Thursdays and Fridays reveal both a greater average number of posts per day and
a lower percentage of posts that prove most engaging. This information is useful
in presenting the possibility that a weekly rhythm of posting by the movement and
user activity may exist.
21
23
9.5%
22
8.5%
21
7.5%
20
6.5%
19
5.5%
Mon
Tue
Wed
Thu
Fri
AvgPosts
Sat
Percentage of Posts per Weekday that
are Most Significant
(Ln Likes, Comments Above Threshold)
Average Number of Posts per Weekday
Figure 7. Facebook AH, IAC Day-of-the-Week Activity
Sun
PctMostSigPost
Source: Author’s calculations
6. Model Specification
To determine the effect of the variables described in section 5 on Facebook user
activity during our sample period, we estimated multivariate statistical models.
We ran regressions as ordinary least squares with panel-corrected standard errors
accounting for first order autocorrelation. These models measure effects on
Facebook user activity in terms of the percent change in likes or comments,
utilizing various combinations of our independent variables and interaction terms.
The results from these models describe the relationship between these independent
variables and level of user activity. These relationships inform our conclusions on
how varying social media content influences our measure of social engagement.
This model assumes a post’s theme that increases the number of likes or comments
is one that resonates with individuals involved with action on the ground during that
time period.
6.A. Dependent Variables
Using the likes and comments to measure Facebook user activity, as described in
section 3, we had four options to use as dependent variables: number of likes on a
post, number of comments on a post, or the natural log of either of these variables.
From these options, we selected the natural log of likes and natural log of
comments as our dependent variables. We believe that taking the natural log of
the variables for use as the dependent variable in our regression analysis makes
sense for several reasons. Using this method allows us to realize a more normal
distribution of our dependent variable. In a log-linear model, the presence of a
given content variable is represented by a coefficient reflecting the percent, rather
than unit, change in the dependent variable. Use of percent change helps us
compare the effects of a specific theme or event on user activity across the
22
movement. Given that support grew for the movement over time and more users
joined our sample Facebook pages, percent change is a more useful measure. For
example, an increase of ten likes in phase 1 may be large, but only a drop in the
bucket during phase 6, but if we find that the same theme caused a 5 percent
increase in activity in those phases, we know that the variable has a similar effect,
just at a different order of magnitude.
Liking and commenting on a post are fundamentally different actions that a
Facebook user can take. Although the number of likes and comments appears
to be highly correlated over time, these two dependent variables could provide
somewhat different results in our regression models. Commenting on a post
represents a higher level of commitment than liking it, as the user must form and
publicly share an opinion rather than make a single click. However, likes may be
a stronger measure for several reasons. A Facebook user can only like a post once,
so the number of likes is the number of people who liked the post. Facebook users
can comment on a post an unlimited number of times, so a post could potentially
have a high number of comments but a low number of commenters. We also
know that a like is a demonstration of positive agreement or support for a post.
Comments can be positive or negative about the post or not be related to the
top-level post at all, which makes interpretation of results less clear.
6.B. Independent Variables
We developed a number of independent variables for use in our models. The
variables of greatest interest are the theme variables reflecting the message being
presented by Facebook content at any given point in the movement. We interacted
these theme variables with phase variables, allowing us to analyze changes in
theme effect on user activity.
Other variables included in the models are: post characteristics, government
actions, major protests, the day of the week the post was created, a categorical
variable for the phase in which the posts were written, and interactions between
each theme and phase. The interaction variables help us identify how the effects
of these themes differ across the phases of the movement. The codebook
describing these variables more fully is presented in Appendix E, and summary
statistics are presented in Appendix F: Summary Statistics.
6.C. Regression Models
For this analysis, we developed two regression models using different combinations
of our independent variables to analyze factors driving Facebook user activity. The
basic versions of our model (models 1 and 2) include the variables for the theme of
the post, the type of media the post used, real-world events that took place, the
Facebook page where the post was made, the phase the post was written, and the
day of the week it was posted. The interaction versions of our model (models 3 and
4) incorporate the interaction of theme and phase variables to analyze potential
differences in the importance of different themes over time. We ran each of our
23
regression models twice, once with the natural log of likes as the dependent variable
and once with the natural log of comments as the dependent variable.
In addition to these models, we developed a basic model to test for temporal
causality between Facebook activity and government action. The results of this
model, elaborated on in Appendix G, suggested a strong Facebook response to
negative government action. We see no persuasive link to suggest that Facebook
activity alone compels government action—either positive or negative. As such,
these models are not the focus of our work. They should be viewed as supplement
to this analysis and as a base for further evaluation of the causal relationship
between social media content and real-world action.
Our four econometric models are:
1
3
2:
=
+
+
+
4:
ℎ
ℎ
=
+
+
+
+
+
+
+
+
+
ℎ
+
+
+
+
+
+
ℎ
+
+ +
+
+
∗ ℎ
+
∗ ℎ
+
∗ ℎ
∗ ℎ
+ ∗ ℎ
∗ ℎ
7. Model Estimation and Inferences
This section presents the results and outlines general implications of our analysis.
We discuss the importance of the identified themes as they relate to Facebook user
activity and how the effect of specific themes changed over the course of our
sample period. In our discussion of results in section 7.A., we focus on the models
using the natural log of likes as the dependent variable (rather than the natural log
of comments) because of the advantages in interpretation discussed in section 6.A.
Still, as Facebook only offers users a like button, Facebook user reaction to posts
that generate a negative response may be better measured by comments than likes.
Table 3 and Table 4 summarize the statistically significant (p<0.05) variables
of interest in the models. Percent changes listed in the tables below reflect the
percent change in the expected number of likes (or comments) when a given post
contains the variable listed. For example, as shown in Table 3, the “hunger strike”
variable results in a 4.4 percent increase in the number of likes, which means
that a post containing the theme “hunger strike” is expected to have 4.4 percent
24
more likes than a post not containing that theme, all else constant. Full regression
results of models are included in Appendix H. The unit of analysis in these
models are individual Facebook posts. Both models that use the natural log of
likes for the dependent variable have a high R2, which suggests these models
explain the change in user activity over the course of the movement on these
two Facebook pages fairly well.
Table 3. Basic Model Summary Results,
Statistically Significant Variables of Main Interest
Percent Change in
the Likes on a Post
Percent Change
in Comments
on a Post
Hazare
-4.3%
-4.2%
Hunger Strike
4.4%
RW Protest Event
7.7%
-8.7%
RW Positive Gov’t Act
14.1%
15.2%
Explanatory Variables
RW Negative Gov’t Act
15.0%
RW Parliament Session
21.2%
Video
4.5%
7.4%
Wednesday
8.8%
15.0%
Thursday
13.1%
16.5%
Friday
10.9%
16.1%
Source: Authors’ calculations
25
Table 4. Interaction Model Summary Results,
Statistically Significant Variables of Main Interest
Explanatory Variables
Percent Change in
the Likes on a Post
Corruption Phase 5
Percent Change
in Comments
on a Post
7.0%
Demonstration Phase 4
10.0%
Government Phase 6
29.8%
Hazare
-60.6%
-56.7%
Hazare Phase 2
-9.4%
-0.9%
Hazare Phase 3
-9.5%
-5.6%
Hazare Phase 4
2.7%
-2.3%
Hazare Phase 5
3.8%
9.2%
Hazare Phase 6
4.8%
3.1%
Hazare Phase 7
8.4%
0.1%
Hunger Strike Phase 5
-20.9%
Hunger Strike Phase 6
-34.8%
Lokpal Phase 2
9.6%
Lokpal Phase 3
8.8%
Lokpal Phase 6
28.9%
Lokpal Phase 7
10.9%
RW Protest Event
14.4%
RW Positive Gov’t Act
13.3%
RW Negative Gov’t Act
14.2%
15.0%
RW Parliament Session
7.8%
-16.0%
Video
5.3%
7.9%
Wednesday
9.1%
15.3%
Thursday
14.0%
17.8%
Friday
11.0%
16.5%
Source: Authors’ calculations
Running our regressions using basic ordinary least squares (OLS) regressions
produced results suffering from heteroscedasticity and autocorrelation. To correct
for these two issues, we ran these regressions as OLS with panel-corrected standard
errors accounting for first order autocorrelation. These models may still encounter
problems with reverse causality between the dependent variable and the variables
for real-world events occurring on the ground. We discuss the results of the
diagnostic tests for these problems more fully in Appendix I: Diagnostic Tests and
Limitations of the Models.
26
7.A. Regression Model Inferences
The regression models account for a wide range of factors potentially influencing
the amount of user activity on these two Facebook pages. The results suggest
which themes were most significant and how the different themes correlated with
increased user activity at different points in time. Analysis of the type of content
most likely to result in higher levels of user activity at a particular moment in the
anticorruption movement provides insight into how a social media movement can
be both monitored and potentially managed. Additionally, these results suggest
real-world events are an important factor in the level of user activity.
Several themes had a significant impact on the rate at which Facebook users
interacted with the posts, both overall and across time. Posts about Hazare himself
led to a decrease in the rate of Facebook user action overall, while posts about
hunger strikes significantly increased the rate of Facebook user interaction. No
one theme variable remained significant throughout the course of the entire
movement (in all seven phases).
Some themes were important during specific phases of the movement. Posts about
Hazare initially decreased the rate of user interaction with the post. However,
during phase 4, Hazare’s arrest led to a large protest. Following the arrest and
subsequent hunger strike, more posts about Hazare correlate with an increase in
the rate of Facebook user activity. This positive relationship grew throughout the
rest of the movement. This growth suggests that August may have been a tipping
point when Hazare became a key figurehead of the movement. In this case, the
government action to prevent him from protesting may have legitimized him as a
public figure and helped him gain support for the movement.
Early in the movement, an increase in social media reports about the Lokpal bill
itself correlates to an increased rate of user activity. In April, during Hazare’s first
hunger strike, posts about the Lokpal bill had a large positive effect on post
interaction rates. This effect continued into phase 3 while the Joint Drafting
Committee was meeting regularly to draft a Lokpal bill to bring before
Parliament. However, the Lokpal bill’s importance drops off during the August
protest, and it doesn’t become important again until the beginning of 2012 when
the Rajya Sabha discusses the Lokpal bill following its passage in Parliament’s
lower house.
Finally, posts that encouraged Facebook users to rally or become involved in the
movement in April and August resulted in an increase in Facebook user
interaction during the subsequent protests. This significance was not evident,
however, during Hazare’s hunger strike in December. This effect is reflected in
the real world by significantly lower protester turnout in December compared to
previous protests and could be related to Hazare calling off his protest
prematurely.
27
Some of the characteristics of the posts were also important. The addition of a
video increased the rate of Facebook user interaction. Posts on the AH page also
generated a significantly higher rate of interaction per post than posts on the IAC
page. Finally, posts created on Wednesday, Thursday, and Friday appear to have
a higher rate of activity than during the rest of the week.
The variables with the largest impact on the rate of user interaction were the
variables coding the occurrence by date of organized protests, and dates the
government publically responded to the movement or took actions related
to it. During protest periods, the rate of Facebook user interaction increased
significantly. It also increased during periods when the government took action,
regardless of whether it targeted its actions against the movement and its leaders
or whether it gave in to and met movement demands. Likes also increased when
Parliament was in session. These real-world events seem to induce individuals to
log onto Facebook to get information, where they could interact with the
messages and information shared by the movement and other Facebook users.
This finding may suggest that the movement had its largest audience and could
most effectively share its message during periods of on-the-ground action.
7.B. Differences Between Natural Log of Likes
and Natural Log of Comments Regression Results
Although the results of models using the natural log of likes as the dependent
variable and models using the natural log of comments as the dependent variable
have many similarities, some significant differences remain. These differences
could be attributed to the fact that “liking” and “commenting” on Facebook are
fundamentally different actions. These differences could explain the differences in
users’ responses to posts over time when comparing likes and comments. This
finding could be particularly important for certain themes or time periods where
Facebook users might respond more negatively to the content of posts. Although
the natural log of likes appears to be a better metric to use in these regressions, the
results of the models that use the natural log of comments are worth exploring.
The most interesting difference between the models is the results for the realworld negative government action variables. The models that use the natural log
of comments find that when the government took actions contrary to what the
anticorruption movement wanted, the rate of commenting increased significantly.
When the natural log of likes is the dependent variable, however, the real-world
negative government action variable is insignificant. This finding likely reflects
the fact that Facebook users can respond negatively to posts only through
comments, not through likes. This model also found real-world protest events and
parliamentary sessions to be insignificant or negatively related to the rate at which
users commented on these posts.
The natural log of comments interaction model identified additional themes as
significant drivers of user activity in several phases. It also found a few themes
that had been identified as important by the natural log of likes model to be
28
insignificant. The natural log of comments model found that discussion of the
Lokpal bill did not significantly affect the rate of Facebook user interaction in
phases 2, 3, or 7. Instead, it suggests that discussion of the Lokpal bill correlated
with decreased Facebook user interaction in phase 6 when Parliament was
debating the Lokpal bill. Posts that discussed the hunger strikes had a significant
effect, demonstrated by a reduction in Facebook comments during phases 5 and 6.
The comments interaction model also found that posts about corruption were
insignificant in all of the phases except phase 5, the period between Hazare’s
August and December hunger strikes. Finally, this model suggests that posts
about the Indian government were related to an increase in the rate of Facebook
user interaction in December while Parliament debated the Lokpal bill.
8. Summary and Recommendations for Further Work
To collect data, we performed a census of top-level post content from two
significant and widely followed Facebook pages. The nature and scope of our
data, however, are inadequate to robustly investigate or demonstrate causal
relationships between Facebook activity and subsequent real-world actions, as
discussed in Appendix G. Data on user behavior beyond the top-level posts would
enable investigation of the influence of individual Facebook users in a social
movement. As Facebook records and publishes the identity of each user who likes
or comments on a post and links the activity to that user’s profile, researchers may
be able to develop a tracking mechanism to record the frequency of likes and
comments per individual, catalogue and correlate the temporal and thematic
nature of those interactions, and explore the profiles of the most-engaged or mostinfluential individuals. In so doing, the researcher could determine the extent of
an individual’s own friend circle. The researcher could learn about an individual’s
stated attendance or role in protest activities and ability to mobilize friends to
attend or participate in another way. Skilled computer programmers and library
science personnel should be able extract this information through the native
Facebook Application Programming Interface and organize it in an analytically
useful way. Appendices J and K present potential approaches to expanding the
universe of data collection.
This is not to say that the dataset we created is without value. Information gained
through the collection and analysis of Facebook data does support several
important conclusions concerning the role social media played in the 2011-12
anticorruption movement in India. Using user activity as a measure of social
engagement with real-world action, we discuss potential implications of our
results for future social movements. Our statistical models demonstrate that realworld events effects on Facebook are consistent in practical and statistical
significance. This important observation supports the conclusion that spikes in
user activity are not principally driven by the incorporation of thematic language
or multimedia links but rather by real-world actions taken by the movement and
the government. People turn to Facebook (1) to get information from a primary
source; (2) to learn how a movement will engage with or respond to government
29
actions; and (3) to identify ways in which they themselves can become engaged—
online or on the ground—within the movement.
Real-world actions drive thematic language use on Facebook. For example, the
natural log of likes regression demonstrates that Lokpal was significant as a theme
in phases 2, 3, and 7. This result is understandable in the context of the
movement, as these are phases in which the government took action that could
lead individuals to believe progress would be made on the bill. The announcement
of the formation of the Joint Drafting Committee occurred in phase 2; the
committee met during phase 3; and users responded to the passage of the Lokpal
bill in phase 7. These findings demonstrate the regression analysis identified the
most significant themes at a given point in the anticorruption movement.
Throughout the movement as social momentum grew during the various action
phases, user activity peaked and then sharply decreased following some form
of government action. These results suggest that positive government action can
increase user activity for a short period of time when Facebook users discuss
the government action or concession. However, government action may halt
or reduce real-world protest action as social activists wait for the government
to fulfill a promise or concession. This waiting is reflected by a temporally
correlated decrease in user activity. Reaction on Facebook holds until the
movement decides that by protesting again it may reenergize a stalled
process or receive further concessions from the government.
Government actions taken to repress the movement were significant in the natural
log of comments model regression, and they correlated with an increase in the
number of comments. Negative government action was not significant in the
natural log of likes model. This difference makes sense when we consider the
nature of the like action. A Facebook post detailing an unfavorable government
action would garner fewer likes than a neutral or positive government action
simply because movement supporters likely would disagree with the activity and
therefore decline to like it. Thus, a post about a negative government action would
not be expected to induce a large number of likes but would more likely lead to an
increase in the number of comments as Facebook users discuss the implications of
the action. Contrary to positive government action, which may lead to temporary
lulls in user activity, negative government actions seem to act as catalysts,
increasing social media momentum and triggering further on-the-ground
responses if the negative action is not effective in forcing the movement
to change direction or fail at achieving its goals.
Understanding the distinction between positive and negative actions and their
consequences is extremely important for any government looking to respond to a
social movement. Government actors can also understand the inherent risk in a
given action. For example, the police arrest of Hazare in August set off the largest
spike in Facebook activity of the entire surveyed period and a major push of realworld protest activity. In contrast, the Lok Sabha’s passage of the weaker Lokpal
bill on December 27 led the movement to use Facebook to get out the message
30
that the movement was reorganizing and shifting focus. The government’s
positive action on the legislation (albeit still perceived as inadequate by some)
undermined movement support and created an identity crisis for IAC leaders
who in turn changed the goals and direction of the movement.
As for organizers of social movements, they could track Facebook user activity to
help identify actions and events that were influential enough to garner responses
from news media and the government. A movement could monitor the response to
the themes organizers feel are critical to advancing the message. Tracking activity
related to specific posts could help the movement to better define its message and
purpose and better use themes that resonate most with Facebook users. Matching
the message to the audience is a primary task of any movement looking to grow
support. While keeping on message by creating content around the most important
theme at a specific time, the movement would need to be aware of the potential
for “post fatigue.” We see the AH page received a larger number of likes and
comments per post relative to the IAC page even though IAC posted many more
times per day, which may reflect that users only interact with a certain amount of
content each day. A movement might want to limit the number and topics of
messages it shares.
Overall, we view Facebook and other forms of social media as excellent organizing
tools for social movements. The content and focus of the posts provide a daily
insight into the function of the movement and the message it is trying to advance.
Paying attention to this content can help participants and observers better evaluate
messages reported by government or traditional news sources. Understanding
Facebook content can help observers make a “best guess” at what a social
movement might plan for its next move and thus have time to develop a response.
We present possible methodological options and issues in appendices J and K.
Additionally, social media activity can be used to identify important events in a
movement. Analysts can focus on identified dates or events of interest to more
closely observe what occurred and how events may shape government and
social movement responses. Analysts can use Facebook almost instantaneously
for such observations given the real-time posting of massive amounts of
content. Changes in content and message can be identified quickly via social
media. The instantaneous nature of social media provides information that
differs from that found in news media.
This initial look into the use of Facebook by India’s anticorruption movement
in 2011-12 can have implications for future social movements. The metrics of
measurement can serve as a template for analysis of ongoing social movements.
The benefit of this form of analysis is the wide, free, and instantaneous
availability of social media data and content. Themes specific to a movement
can be quickly identified and tested. Lists of themes can expand as the movement
progresses. For broader implementation, we recommend a real-time gathering and
coding of data so that points of interest and key events identified by spikes in
Facebook and social media user activity can be identified quickly, an approach
31
that may allow sufficient time to craft responses. This ongoing real-time analysis
allows for immediate insights about actions and responses, as well as a reflective
look at the movement’s history as recorded on Facebook. Although further work
should be done to identify the existence of any causal relationships between social
media content and on-the-ground action, we are certain that valuable information
about a particular social movement can be gleaned from this type of data using the
method described in this analysis.
32
Appendix A: Narrative Timeline
2010
Jan 6
2011
Jan 30
Feb 26
Mar 7
Apr 4
Apr 5
Apr 8
Apr 9
Apr 9
Apr 16
May 2
May 7
May 13
May 23
May 30
Jun 1
Jun 3
Jun 4
Jun 5
Jun 6
Jun 12
Jun 15
Jun 16
Jun 20
Government drafted a version of the Lokpal bill. Social activists said it was not
strong enough and drafted their own bill. India Against Corruption (IAC) formed
to lead fight against corruption.65
Indian government formed Group of Ministers to consider measures that
government could take to tackle corruption.66
Marches were held in more than 60 cities to demand anticorruption Lokpal bill.
Social reformer Anna Hazare participated in New Delhi rally.67
Hazare announced fast unto death from April 5 if Prime Minister Manmohan
Singh did not cover civil society members in the Lokpal bill.68
Hazare and colleagues met with the prime minister, law minister, and other
senior officials. Both sides agreed to have a sub-committee of the Group of
Ministers discuss the draft.69
Prime minister's office announced its "deep disappointment that Shri Anna
Hazare is still planning to go ahead with his planned hunger strike."70
Hazare began first hunger strike for stronger Lokpal bill.71
Government agreed to draft stronger bill and form a joint committee.72
Indian government issued a notification creating a joint drafting committee,
charged with preparing a draft of the Lokpal bill.73
Hazare's first fast ended, and he set August 15 deadline for Parliament to pass
bill.74
First meeting of the Joint Drafting Committee to draft the Lokpal bill.75
Second meeting of the Joint Drafting Committee to draft the Lokpal bill.76
Third meeting of the Joint Drafting Committee to draft the Lokpal bill.77
India ratified the United Nations Convention against Corruption.78
Fourth meeting of the Joint Drafting Committee to draft the Lokpal bill.79
Fifth meeting of the Joint Drafting Committee to draft the Lokpal bill.
Disagreement arose over who should be included.80
Social activist Baba Ramdev came to New Delhi to start the “Satyagraha against
Corruption” campaign. Met with four senior ministers to discuss his issues and
demands.81
Baba Ramdev and the government completed talks; both sides claimed to have
reached a consensus.82
Despite agreement, Baba Ramdev held protest at the public square Ramlila
Maidan in New Delhi.83
Police raided Baba Ramdev’s protest site during the night, detained him, and
removed his supporters using tear gas.84
Sixth meeting of the Joint Drafting Committee to draft the Lokpal bill. Civil
society members boycotted in protest of detention of Baba Ramdev.85
The government disclosed details of wealth, assets, and funds Baba Ramdev
owns but cannot explain how they were obtained. Baba Ramdev lost his
following, ending his protests.86
Seventh meeting of the Joint Drafting Committee to draft the Lokpal bill.87
Joint Drafting Committee talks remained inconclusive, and Hazare announced
he will hold a hunger strike on August 16.88
Eight meeting of the Joint Drafting Committee to draft the Lokpal bill.89
33
Jun 21
Jul 3
Jul 12
Jul 28
Jul 28
Jul 30
Aug 1
Aug 3
Aug 4
Aug 4
Aug 13
Aug 14
Aug 15
Aug 16
Aug 16
Aug 17
Aug 17
Aug 18
Aug 23
Aug 23
Aug 24
Aug 25
Aug 27
Aug 28
Sep 2
Ninth meeting of the Joint Drafting Committee to draft the Lokpal bill. The
meeting ended with large disagreements between parties on scope of the
Lokpal bill.90
All-party meeting on the Lokpal bill yielded agreement that the government
should bring a strong and effective Lokpal bill before the next session of
Parliament and follow established procedures.91
Court charged Hazare in a case of alleged misappropriation of funds.92
Cabinet approved the Lokpal bill.93
Hazare rejected Joint Drafting Committee’s version of the Lokpal bill and
announced his intent to begin the hunger strike on August 16 as planned.94
Police denied Anna Hazare’s movement permission to stage a protest at the
Jantar Mantar observatory.95
Public interest litigation filed in Supreme Court against Hazare to restrain him
from going on a fast because demands are "unconstitutional" and amount to
"interference in legislative process."96
IAC volunteers burn copy of Lokpal bill near Sampurnanand Sanskrit
University.97
Lokpal bill introduced in the Lok Sabha.98
Team Anna members burn copies of Lokpal version introduced in Lok Sabha.99
Prime minister replied to a letter from Hazare, directing Hazare to take his
grievances about the restrictions placed on his fast to the police.100
Congress Party leaders launched a negative information campaign against
Hazare, accusing him of being corrupt. The personal accusations are withdrawn
the next day.101
Prime minister gave Independence Day speech. He devoted one quarter of the
speech to corruption and the Lokpal bill.102
Hazare arrested just before hunger strike begins and taken to Tihar Jail. This
arrest sparked large protests in numerous locations.103
Hazare released from jail but refused to leave and continued fast in jail.104
Police negotiated with Hazare on terms of his public hunger strike. Hazare
refused to leave jail until a deal was struck early on August 18.105
Prime minister and leaders of the Congress Party suggested a "foreign hand"
(an allusion to the United States) behind Hazare's campaign.106
Hazare left jail and continued hunger strike at Ramlila Maidan as huge rallies
and protests continued.107
Prime minister wrote to Anna and requested he end his fast for the sake of his
health and reiterated the government's intention to pass the best possible
Lokpal bill.108
Hazare agreed to hold discussions with the government and representatives of
both parties.109
All parties met in Parliament on Hazare and Lokpal bill.110
Parliament agreed to debate all versions of Lokpal bill.111
Both houses of Parliament adopt a resolution of the Lokpal bill.112
Government agreed to pass Lokpal bill, and Hazare ended his second hunger
strike.113
Income tax office issues notice to chief Hazare aide Arvind Kejriwal for overdue
taxes; Team Anna calls it a “dirty trick,” while government insists it is a “routine
affair.”114
34
Sep 3
Sep 6
Sep 28-29
Oct 16
Oct 17-18
Nov 4
Dec 7
Dec 9
Dec 11
Dec 11
Dec 13
Dec 15
Dec 22
Dec 27
Dec 27
Dec 28
Dec 30
Jan 3
2012
Jan 8
Jan 11
Feb 2
Feb 4
Key members of Team Anna (Arvind Kejriwal, Prashant Bhushan, and Kiran Bedi)
receive breach of privilege notices for remarks against members of
Parliament.115
Government accepted the Group of Ministers’ recommendations fast-tracking
cases against public servants accused of corruption.116
Seventh meeting of the Regional Anti-Corruption Initiative of the Asian
Development Bank/Organisation for Economic and Co-operative Development
for Asia and the Pacific held in New Delhi.117
Hazare begins maunvrat (vow of silence) for “mental peace and good health.”118
Congress party loses all four special elections; Hazare warns that election results
are sign that party should ensure Lokpal passes during Winter session.119
Hazare ended 19-day vow of silence, threatened another hunger strike, and
vowed to campaign against the majority party in five states if Parliament failed
to pass a strong Lokpal bill.120
Parliament’s Standing Committee meets to adopt Lokpal bill draft report;
decision on prime minister inclusion left to Parliament.121
Standing Committee’s Lokpal report tabled in Parliament; Team Anna urges
protest against “weak” Lokpal.122
Prime Minister invites all party leaders to meet December 14 in light of
conflicting views on Lokpal and pressure from Hazare.123
Hazare sits on daylong fast in New Delhi to protest Standing Committee’s
weaker Lokpal proposals; warns of indefinite fast beginning December 27 if
strong Lokpal does not pass.124
Parliament passes three anti-graft bills aimed at higher-level corruption: Judicial
Standards and Accountability Bill; Grievances Redressal Bill; and
Whistleblowers’ Bill.125
Hazare announced a jail bharo for January 1 if Parliament failed to pass the
Lokpal bill and threatened a hunger strike.126
Government introduced a new comprehensive Lokpal and Lokayuktas (statelevel anti-corruption agencies) bill in the Lok Sabha, withdrawing earlier version
of the Lokpal bill.127
Hazare began a hunger strike in Mumbai in protest of the weaker version of the
Lokpal bill.128
Lok Sabha passed Lokpal bill after ten hours of debate.129
Hazare broke his fast due to poor health and calls off the jail bharo.130
Rajya Sabha adjourned at the end of the Winter session without passing the
Lokpal bill following a 13-hour debate session.131
Hazare announces that he will tour the five states to canvass against political
parties that opposed strong Lokpal bill in the Parliament. Team Anna says the
trip depends on Hazare's health.132
Hazare is discharged from hospital, and he cancels his plan to tour the five
states. He says he will take one month off to rest.133
Hazare’s colleagues decide to tour the five states for a strong Lokpal bill but will
not campaign against any specific party.134
Hazare’s colleagues launch a multi-city campaign in Uttar Pradesh to create
awareness about a strong Lokpal bill but stay away from directly targeting any
one political party.135
Hazare hospitalized again. Hazare’s colleagues continue multi-city campaign.136
35
Appendix B: Gathering Facebook Data
In developing our social media sampling methodology, we considered a variety of
approaches. Within the constraints of reasonable ethical and technical boundaries,
we settled on a manual data collection method known as “screen scraping”
whereby we visited the Facebook page of interest and viewed all top-level posts
on that page for the date range of study, January 27, 2011, to February 19, 2012.
This approach limited our data collection to posts viewable by any registered
Facebook user with no additional content access controls.
With all posts displayed, we saved the data into a native .html file (for archiving
and reference) and .txt file (for further processing). We copied and pasted the
contents of the .txt file into a blank Microsoft Excel workbook, and each line of
text on the web page became a separate row in the workbook.
With the data in Excel, we then went about cleaning, sorting, and categorizing.
We assigned each line of content a unique serial number and Facebook page ID
for future reference and to enable data sorting. Next, we sorted the data by
content so that we could purge any blank rows, rows without relevant content
(punctuation characters, gibberish, superfluous spaces, etc.). We then re-sorted
the data by serial number into original sequence. Using a mix of Excel’s in-built
features and functions, we extracted the date- and time-of-post information; the
number of likes, comments, and shares per post. Using Excel “COUNTIF”
formulae, we developed keyword-specified thematic variables to characterize the
content of each post into social media reference categories we identified as critical
to the movement. These include six substantive themes we identified through
reading posts and studying the movement: Hazare, corruption, lokpal, hunger
strike, demonstration, and government. The reference categories also included
five post-content types: video, blog, news (traditional), web, and photo. Finally,
we wrote macro code using Microsoft Visual Basic to automate the process of
summarizing the per-post data. A documented version of the Visual Basic code is
provided in this appendix, and full specification of thematic keywords is provided
in Appendix E.
Step-by-Step Instructions for Data Collection
We used the following method to collect data from the Facebook pages and create
Excel spreadsheets.
1.
2.
3.
4.
Open Mozilla Firefox
Navigate to Facebook (http://www.facebook.com/)
Log in
In the Facebook Search box, enter the target page/terms
a. Ex. India Against Corruption (IndiACor)
5. On the wall, scroll your screen to view the posts in the date range of interest
a. Continue clicking “Older Posts” until the entire date range is displayed
36
6. Once full date range is expanded, press CTRL-A to select all contents of the
browser window
7. When selected, press CTRL-C to copy all contents into memory
a. AVOID Right-Click / Copy or risk losing all post expansion work with
a misplaced click
8. Open Windows Notepad
9. Press CTRL-V to paste all contents into Windows Notepad
a. This step removes all formatting from the text for ease of use in Excel
10. Save As Notepad file to Dropbox folder with Encoding “UTF-8” and file
name [Website-Page-yyyy-mm-dd].txt
a. Ex. Facebook- IndiACor -2012-02-23.txt
11. Once pasted, press CTRL-A to select all contents of the text document
12. When selected, press CTRL-C to copy all contents into memory
13. Open Microsoft Excel, and click on cell A1
14. Press CTRL-V to paste all contents into the Excel spreadsheet
a. Pasted contents should automatically fill by line in Column A
15. Save Excel file to Dropbox folder with file name [Website-Page-yyyy-mmdd].xlsx
a. Ex. Facebook- IndiACor -2012-02-23.xlsx
16. Return to Mozilla Firefox, and click the orange Firefox Menu
17. Select “Save Page As”
a. This step retains all formatting from the web page for later use
18. Save web page to Dropbox folder with type Web Page, complete and file
name [Website-Page-yyyy-mm-dd]
a. Ex. Facebook-IndiACor-2012-02-23
19. Firefox will create an HTML document and a folder of all images, scripts, and
stylesheets
20. Data are now archived and ready for further processing
Summarizing Facebook Data in Excel with Visual Basic Macros
We used the following code to automatically code and summarize the scraped
Facebook data in Excel using Visual Basic macros.
Sub insertRowSummarizeData()
Application.ScreenUpdating = False
‘To insert a blank space between wall posts’
Do
If InStr(1, Selection.Value, "Write a comment...") > 0 Then
Selection.Offset(1, 0).Select
Selection.EntireRow.Insert
End If
Selection.Offset(1, 0).Select
Loop While Selection.Value <> ""
Dim lngLastRow As Long, _
37
lngFormulaRowStart As Long, _
lngFormulaRowEnd As Long
Dim rngCell As Range
lngLastRow = Cells(Rows.Count, "A").End(xlUp).Row + 1
lngFormulaRowStart = 2
lngFormulaRowEnd = 2
‘For columns with data to COPY from the cell above [ID, page]’
For Each rngCell In Range("A2:A" & lngLastRow)
If Len(rngCell.Value) = 0 Then
lngFormulaRowEnd = rngCell.Row - 1
rngCell.Value = rngCell.Offset(-1, 0).Value
lngFormulaRowStart = rngCell.Offset(1, 0).Row
lngFormulaRowEnd = rngCell.Offset(1, 0).Row
End If
Next rngCell
‘For columns with data to SUM [date, time, like, share, comment, & variables]’
For Each rngCell In Range("C2:C" & lngLastRow)
If Len(rngCell.Value) = 0 Then
lngFormulaRowEnd = rngCell.Row - 1
rngCell.Formula = "=SUM(C" & lngFormulaRowStart & ":C" &
lngFormulaRowEnd & ")"
lngFormulaRowStart = rngCell.Offset(1, 0).Row
lngFormulaRowEnd = rngCell.Offset(1, 0).Row
End If
Next rngCell
Application.ScreenUpdating = True
End Sub
38
Appendix C: Facebook Mechanics
The mission of Facebook is “to give people the power to share and make the
world more open and connected.”137 Anyone older than 13 years with an e-mail
account can sign up for a free Facebook account. The account holder can then
enter personal information, including education, work experience, relationship
status, and contact information on his or her Facebook profile. Users add friends
to construct actual social networks. New account holders can search for people
they know and send friend requests by clicking
. The recipient of the
friend request has the option to confirm the friend request, which allows the
sender to view the profile or hide the request. The new account holder may also
receive friend requests, which he or she can confirm or hide.
Two basic functions of Facebook most relevant to this analysis are posting on
walls and joining groups. Wall posts can originate from the profile owner, in
the form of status updates, or from the owner’s friends. Content of a wall post,
whether a status update or a post by a friend, entirely depends on the poster,
and it can range from simple text to links to outside websites, videos, or images.
Any account holder can like, share, or comment on posts. An account holder can
upload photographs or videos for friends to view. Account holders can also be
“tagged’ in friends’ photos. A “tag’ is an identifier for the individuals associated
with the photograph. Individuals can also join groups and, in doing so, gain
access to the group profile. The group profile may serve as a conduit for mass
communication between group organizers and members through sending
messages.
An important part of Facebook’s operation is privacy. Individual account holders
can arrange their security settings to customize how much information is
conveyed to non-friends. Additionally, account holders have the option of
choosing privacy settings for content that friends would otherwise be able to see,
such as individual wall posts or photographs. Privacy settings for wall posts or
photographs can be set to allow viewing by certain friends. Groups can exercise
privacy controls through membership, in that organizers establishing an “open”
group allow any Facebook account holder to join, while “closed” groups allow
new members by invitation.
39
Terms
Top-level post: We use the term “top-level post” to refer to any post on a
Facebook group page posted by the page owner or by a user authorized by the
page owner to post. Top-level posts may receive visitor likes, comments, and
shares. In this analysis, on the IAC page, someone affiliated with IAC who holds
security permissions for the account writes and posts a top-level post. For
example:
Like: A like is an indicator of approval or agreement with a Facebook post
signified by a “thumbs up” icon and generated by clicking on the “like” link. For
example:
Running
‘Like’
count
A Facebook user
can “like” this
post by clicking
this link.
The ability to like something typically depends on a page’s privacy settings. In the
case of Anna Hazare and IAC community pages, “open” privacy settings allow
for anyone to access the site and like its posts. Individual Facebook users can
“like” any number of posts, but a user can only “like” the same post one time.
Comment: Comments function as written responses to wall posts. They can be
positive, negative, or addendums to the original post. For example:
40
Users can
comment
by clicking
the
‘Comment’
link or
typing
here.
Running
comment
count
Share: When a Facebook user shares another user’s post, the post appears on the
sharer’s wall in the same way the profile owner would write a post. A description
at the top of the post states that the profile owner shared the post, and the content
of the post is visible to all users who have access to the sharer’s profile. For
example:
41
Facebook
users can
share this
post with
friends
by
clicking
“Share.”
Number
of shares
Clicking
on this
icon will
show
who
shared
this post.
Scrape/scraping: Scrape or scraping refers to the act of copying and pasting the
content of a Facebook page.
The following is how content appears in plain text once scraped off of a Facebook
page:
India Against Corruption
The accused had to spend nine more days behind bars since all
government offices including the courts were closed due to Eid holidays
that ended on Sunday.
Hazare supporters granted bail in Dubai
www.rediff.com
The public prosecution in Dubai has granted bail to five Anna Hazare
supporters who were jailed for violating United Arab Emirates national
security laws on August 21...
Like · · Share · September 4, 2011 at 11:29pm ·
760 people like this.
42
Appendix D: News Media Data Collection
To incorporate a quantitative analysis component into our comparison of social
media to two online newspapers, we collected data on the number of articles
published per day about the anticorruption movement from The Times of India
and The Hindu. We were limited in our ability to collect data from other news
sources, such as The Hindustan, because their articles were not available in online
databases for the entire period of our study. We did not include any newspapers
that were not in English because of language restrictions.
The data on the volume of articles from The Times of India and The Hindu do not
include all of the articles they published about the movement. While searching on
the newspaper’s online archives on their respective webpages, we limited our
search to articles that mentioned Hazare or the Lokpal bill. As a result, other
articles related to other aspects of the movement could have been missed. In
addition to this problem, some articles identified by the search engine on those
sites were not directly related to the movement, although we tried to exclude these
articles from our article count. Finally, many of the same articles were listed
multiple times on the same day or on consecutive days in the website search
results. As a result, these data on the volume of news articles per day should be
taken as a way to observe the overall changes in volume but not as an accurate
portrayal of the exact number of unique articles written on each day.
Case Study of Problems with this Data Collection Method:
December 5 – December 9
In early December, the Indian Parliament discussed the Lokpal bill. Longstanding differences of opinion on inclusion of the Indian prime minister
under the authority of the Lokpal, the role of the Central Bureau of Investigation,
and whether the lower-level bureaucracy should be overseen by the central
government anticorruption agency or its equivalents at the state level (the
Lokayuktas) filled the debate.138 Anna Hazare and his supporters engaged with
news media to promote events and protests to support a stronger Lokpal bill as
well as to publicly criticize parts of the current bill that did not meet their
expectations.139
Although content of news articles during this four-day period suggests a significant
upcoming event in the anticorruption movement, particularly a vote on the Lokpal
bill, the news media article counts for The Hindu and The Times of India do not
increase significantly above the average number of articles per day. As can be seen
in Table D-1, only the combined article tally on December 8 comes close to
approaching 22.5, the average number of Facebook posts per day over the
movement. However, the high level of Facebook activity draws attention to this
time period. With the exception of December 5, Facebook likes and comments
exceed their combined daily averages (15,163.1 and 2,902.1 respectively) for
December 6 through December 9.140 Though averages are very basic metrics,
43
this contrast in activity suggests that the two newspapers in this period did not
cover a story that attracted significant attention on Facebook.
Table D-1. Volume Comparison Between News Media and Facebook Activity
Date
5 Dec
6 Dec
7 Dec
8 Dec
9 Dec
News Media
10
17
15
20
18
Posts
18
26
28
32
29
Source: Authors’ calculations
Likes
11,285
27,908
25,254
23,274
18,457
Comments
2,002
7,019
3,899
2,913
2,954
Strong reactions to Telecommunications Minister Kapil Sibal and his attempts
to encourage Internet companies to prescreen site content could be an issue
motivating these higher levels of social media activity that may not have
warranted the same levels of attention from news media sources.141
On December 5, the AH page had no posts, but typical “like” traffic on the IAC
page tended to be between 400 and the upper 600s, with posts about plans for
December 11 rallies attracting much of the attention. To contrast, an IAC post
linking to an article in the New York Times about the Indian government asking
Internet companies and social media to prescreen site content in an effort “to
remove disparaging, inflammatory or defamatory content before it goes online”
gathered 1,084 likes.142 By the following day, likes on posts about the Indian
government’s “battle” on Internet content were well into the 2,000s. A post at
10:31 a.m. linking to an article in The Times of India about Google refusing to
remove controversial material from its sites gained 2,817 likes and a “Way to go,
Google!!” 143 These posts were accompanied by a 1:27 a.m. post on Anna
Hazare’s Facebook page attracting 2,361 likes that stated, “Kapil Sibal wants to
censor Facebook and other social media. The government is making all attempts
to block off communication channels for the anticorruption movement.” In
comparison, a December 6 post on the IAC page at 1:00 p.m. about Anna
Hazare’s proposed December 11 debate with political party leaders, which had
already started attracting news media coverage, gained 660 likes. Contrasts
between activity on censorship-related posts and posts on the Lokpal or Hazare’s
upcoming fast continued throughout most of the time period.
A drawback to measuring news media volume is the exclusion of articles
mentioning Sibal and censorship when using the search keywords “Anna Hazare”
and “Lokpal.” Using these terms, our search of The Hindu and The Times of India
turned up only three articles that mentioned Sibal and the Internet. However,
when conducting a search using keywords more specific to the issue (person name
“Kapil Sibal” and topic “censorship”), article counts appear fairly similar, as
presented in Table D-2. When news articles from these two sources are combined,
all of them but December 5 are significantly above average.
44
Table D-2. Comparison of News Media Keywords
Date
5 Dec
6 Dec
7 Dec
8 Dec
9 Dec
Initial search:
Censorship search:
(“Anna Hazare” and “Lokpal”)
(“Kapil Sibal” and “censorship”)
10
8
17
16
15
11
20
20
18
10
Source: Authors’ calculations
Total News
Articles
18
33
26
40
28
Social media may be more flexible in describing real-world action than news
media because social media better reflect the many factors influencing movement
participation. By restricting our search for news articles to those containing “Anna
Hazare” and “Lokpal,” we limited our ability to identify all of the news articles
relevant to the movement and the people involved. As a result, pure analysis of
our news media volume data would have missed the increase in activity at this
point in time. It could have also failed to identify some issues important to
members of the movement but unrelated directly to the movement itself.
45
Appendix E: Codebook
This codebook includes descriptions of all of the variables in our dataset, though
not all the variables were included in our models.
Identifiers
ID: line in original Excel sheet containing content of all posts
ID2: unique id number for each post, created using Excel
PAGE: specific Facebook page from which the post was scraped
0: Anna Hazare fan page
1: India Against Corruption community page
AUTHOR: author of original post
0: owner of the page
1: all other authors
UNIQUEDATE: number assigned to each original post in sequential order
MON: posts made on Monday (IST - UTC+05:30)
1: posts made on Monday
0: all other posts
TUE: posts made on Tuesday (IST - UTC+05:30)
1: posts made on Tuesday
0: all other posts
WED: posts made on Wednesday (IST - UTC+05:30)
1: posts made on Wednesday
0: all other posts
THU: posts made on Thursday (IST - UTC+05:30)
1: posts made on Thursday
0: all other posts
FRI: posts made on Friday (IST - UTC+05:30)
1: posts made on Friday
0: all other posts
SAT: posts made on Saturday (IST - UTC+05:30)
1: posts made on Saturday
0: all other posts
46
SUN: posts made on Sunday (IST - UTC+05:30)
1: posts made on Sunday
0: all other posts
Activity Variables
LIKES: total number of likes of original post
LNLIKES: natural log of LIKES
COMMENTS: total number of comments on original post
LNCOMMENTS: natural log of COMMENTS.
Theme Variables
CORRUPT: posts discussing the theme of corruption
1: posts containing the words “corrupt,” “graft,” or “black money”
0: all other posts
LOKPAL: posts discussing the theme of the Lokpal bill
1: posts containing the words “lokpal,” “ombudsman,” “jokepal,” or “bill”
0: all other posts
HAZARE: posts discussing Anna Hazare
1: posts containing the words “hazare” or “anna”
0: all other posts
STRIKE: posts referencing the theme of a hunger strike
1: posts containing the words “fast,” “hunger,” or “strike”
0: all other posts
DEMO: posts referencing the theme of demonstrations or calls to action
1: posts containing the words “demonstrat,” “gather,” “meet,” “rally,”
“relay,” “march,” “event,” “burn,” “join,” “missed call,” “vigil,” “street
play,” or “anger of the people”
0: all other posts
GOVT: refers to posts that discuss the theme of government
1: posts containing the words “government,” “parliament,” “congress,”
“joint committee,” “CBI,” “BJP,” “manmohan singh,” “rahul gandhi,”
“sonia gandhi,” “swaraj,” “the house,” “rajya sabha,” “rajyasabha,” “lok
sabha,” or “loksabha”
0: all other posts
47
Content Variables
VIDEO: posts containing a video or link to a video
1: posts containing the words “youtube,” “length,” “webcast,” or “ndtv”
0: all other posts
BLOG: posts containing a link to a blog post
1: posts containing the words “blog” or “wordpress”
0: all other posts
NEWS: posts containing links to news articles
1: posts containing the words “indiatimes,” “hindustantimes,” “thehindu,”
or “dnaindia”
0: all other posts
WEB: posts containing a link to a website
1: posts containing the words “.com,” “.org,” “.net,” “.in,” or “http”
0: all other posts
PHOTOS: posts containing photos or a link to photos
1: posts containing the words “photo,” “picture,” “pics,” or “cdr”
0: all other posts
Phase Variables
PHASE1: posts were made in the first phase
1: posts made in the first phase
0: all other posts
PHASE2: posts were made in the second phase
1: posts made in the second phase
0: all other posts
PHASE3: posts were made in the third phase
1: posts made in the third phase
0: all other posts
PHASE4: posts were made in the fourth phase
1: posts made in the fourth phase
0: all other posts
PHASE5: posts were made in the fifth phase
1: posts made in the fifth phase
0: all other posts
48
PHASE6: posts were made in the sixth phase
1: posts made in the fifth phase
0: all other posts
PHASE7: posts were made in the seventh phase
1: posts made in the fifth phase
0: all other posts
Corruption/Phase Interaction Variables
CORRUPT1: product of CORRUPT and PHASE1
1: posts referencing corruption made in the first phase
0: all other posts
CORRUPT2: product of CORRUPT and PHASE2
1: posts referencing corruption made in the second phase
0: all other posts
CORRUPT3: product of CORRUPT and PHASE3
1: posts referencing corruption made in the third phase
0: all other posts
CORRUPT4: product of CORRUPT and PHASE4
1: posts referencing corruption made in the fourth phase
0: all other posts
CORRUPT5: product of CORRUPT and PHASE5
1: posts referencing corruption made in the fifth phase
0: all other posts
CORRUPT6: product of CORRUPT and PHASE6
1: posts referencing corruption made in the sixth phase
0: all other posts
CORRUPT7: product of CORRUPT and PHASE7
1: posts referencing corruption made in the seventh phase
0: all other posts
Lokpal/Phase Interaction Variables
LOKPAL1: product of LOKPAL and PHASE1
1: posts referencing the Lokpal bill made in the first phase
0: all other posts
LOKPAL2: product of LOKPAL and PHASE2
1: posts referencing the Lokpal bill made in the second phase
0: all other posts
49
LOKPAL3: product of LOKPAL and PHASE3
1: posts referencing the Lokpal bill made in the third phase
0: all other posts
LOKPAL4: product of LOKPAL and PHASE4
1: posts referencing the Lokpal bill made in the fourth phase
0: all other posts
LOKPAL5: product of LOKPAL and PHASE5
1: posts referencing the Lokpal bill made in the fifth phase
0: all other posts
LOKPAL6: product of LOKPAL and PHASE6
1: posts referencing the Lokpal bill made in the sixth phase
0: all other posts
LOKPAL7: product of LOKPAL and PHASE7
1: posts referencing the Lokpal bill made in the seventh phase
0: all other posts
Hazare/Phase Interaction Variables
HAZARE1: product of HAZARE and PHASE1
1: posts referencing Anna Hazare made in the first phase
0: all other posts
HAZARE2: product of HAZARE and PHASE2
1: posts referencing Anna Hazare made in the second phase
0: all other posts
HAZARE3: product of HAZARE and PHASE3
1: posts referencing Anna Hazare made in the third phase
0: all other posts
HAZARE4: product of HAZARE and PHASE4
1: posts referencing Anna Hazare made in the fourth phase
0: all other posts
HAZARE5: product of HAZARE and PHASE5
1: posts referencing Anna Hazare made in the fifth phase
0: all other posts
HAZARE6: product of HAZARE and PHASE6
1: posts referencing Anna Hazare made in the sixth phase
0: all other posts
50
HAZARE7: product of HAZARE and PHASE7
1: posts referencing Anna Hazare made in the seventh phase
0: all other posts
Hunger Strike/Phase Interaction Variables
STRIKE1: product of STRIKE and PHASE1
1: posts referencing a hunger strike or fast made in the first phase
0: all other posts
STRIKE2: product of STRIKE and PHASE2
1: posts referencing a hunger strike or fast made in the second phase
0: all other posts
STRIKE3: product of STRIKE and PHASE3
1: posts referencing a hunger strike or fast made in the third phase
0: all other posts
STRIKE4: product of STRIKE and PHASE4
1: posts referencing a hunger strike or fast made in the fourth phase
0: all other posts
STRIKE5: product of STRIKE and PHASE5
1: posts referencing a hunger strike or fast made in the fifth phase
0: all other posts
STRIKE6: product of STRIKE and PHASE6
1: posts referencing a hunger strike or fast made in the sixth phase
0: all other posts
STRIKE7: product of STRIKE and PHASE7
1: posts referencing a hunger strike or fast made in the seventh phase
0: all other posts
Demonstration/Phase Interaction Variables
DEMO1: product of DEMO and PHASE1
1: posts referencing a demonstration or call to action made in the first
phase
0: all other posts
DEMO2: product of DEMO and PHASE2
1: posts referencing a demonstration or call to action made in the second
phase
0: all other posts
51
DEMO3: product of DEMO and PHASE3
1: posts referencing a demonstration or call to action made in the third
phase
0: all other posts
DEMO4: product of DEMO and PHASE4
1: posts referencing a demonstration or call to action made in the fourth
phase
0: all other posts
DEMO5: product of DEMO and PHASE5
1: posts referencing a demonstration or call to action made in the fifth
phase
0: all other posts
DEMO6: product of DEMO and PHASE6
1: posts referencing a demonstration or call to action made in the sixth
phase
0: all other posts
DEMO7: product of DEMO and PHASE7
1: posts referencing a demonstration or call to action made in the seventh
phase
0: all other posts
Government/Phase Interaction Variables
GOVT1: product of GOVT and PHASE1
1: posts referencing the government made in the first phase
0: all other posts
GOVT2: product of GOVT and PHASE2
1: posts referencing the government made in the second phase
0: all other posts
GOVT3: product of GOVT and PHASE3
1: posts referencing the government made in the third phase
0: all other posts
GOVT4: product of GOVT and PHASE4
1: posts referencing the government made in the fourth phase
O: all other posts
GOVT5: product of GOVT and PHASE5
1: posts referencing the government made in the fifth phase
0: all other posts
52
GOVT6: product of GOVT and PHASE6
1: posts referencing the government made in the sixth phase
0: all other posts
GOVT7: product of GOVT and PHASE7
1: posts referencing the government made in the seventh phase
0: all other posts
Real-World Event Variables
RWPOSGOVACT: posts made on days when the government took action
complementary to the interests of the anticorruption movement
1: posts made on March 7; April 8, 9, 16; May 2, 7, 23, 30; June 3, 6, 15,
20, 21; July 3, 28; August 4, 15, 23, 24, 25, 27, 28; September 6;
December 7, 9, 13, 20, 22, and 27
0: all other posts
RWNEGGOVACT: posts made on days when the government took action
directly or indirectly against the interests of the anticorruption movement
1: posts made on June 5; July 30; August 1, 14, 16, 17; September 2, 3;
and December 30
0: all other posts
RWPROTESTEVENT: posts made while a major anticorruption protest was in
progress
1: posts made on January 29 – 31; April 3 – 14; June 3 – 11; August 3 – 8,
12 – 31; December 9 – 14, and 18 – 31
0: all other posts
RWPARLSESS: posts made while parliament was in session
1: posts made on February 21 – March 25; August 1 – September 8; and
November 22 – December 29
0: all other posts
53
Appendix F: Summary Statistics
The following tables present descriptive statistics for variables in our dataset.
See Appendix E for the codebook that describes the variables.
Table F-1. Identifiers
Variable
Observations
Mean
Standard
Deviation
Minimum
Maximum
id
id2
page
author
uniquedate
mon
tue
wed
thu
fri
sat
sun
8103
8103
8104
8103
8103
8103
8103
8103
8103
8103
8103
8103
0.927
0.956
3803.624
0.135
0.141
0.139
0.155
0.150
0.144
0.137
0.260
0.206
2146.534
0.341
0.348
0.346
0.362
0.357
0.351
0.344
0
0
1
0
0
0
0
0
0
0
1
1
7512
1
1
1
1
1
1
1
Minimum
Maximum
0
0
0
0
10465
9.256
3771
8.235
Minimum
Maximum
0
0
0
0
0
0
1
1
1
1
1
1
Minimum
Maximum
0
0
0
0
0
1
1
1
1
1
Table F-2. Activity Variables
Variable
Observations
Mean
likes
lnlikes
comments
lncomments
8103
8071
8103
7981
727.932
5.917
139.321
4.206
Standard
Deviation
797.655
1.424
204.592
1.352
Table F-3. Theme Variables
Variable
Observations
Mean
corrupt
lokpal
hazare
strike
demo
govt
8103
8103
8103
8103
8103
8103
0.229
0.271
0.315
0.082
0.272
0.180
Standard
Deviation
0.420
0.445
0.464
0.275
0.445
0.385
Table F-4. Content Variables
Variable
Observations
Mean
video
blog
news
web
photos
8103
8103
8103
8103
8103
0.072
0.039
0.147
0.479
0.107
Standard
Deviation
0.259
0.192
0.354
0.500
0.309
54
Table F-5. Phase Variables
Variable
Observations
Mean
phase1
phase2
phase3
phase4
phase5
phase6
phase7
8103
8103
8103
8103
8103
8103
8103
0.179
0.095
0.239
0.177
0.202
0.042
0.066
Standard
Deviation
0.383
0.293
0.426
0.382
0.402
0.201
0.249
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-6. Corruption/Phase Interaction Variables
Variable
Observations
Mean
corrupt1
corrupt2
corrupt3
corrupt4
corrupt5
corrupt6
corrupt7
8103
8103
8103
8103
8103
8103
8103
0.056
0.032
0.065
0.026
0.031
0.008
0.011
Standard
Deviation
0.230
0.177
0.246
0.159
0.175
0.087
0.103
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-7. Lokpal/Phase Interaction Variables
Variable
Observations
Mean
lokpal1
lokpal2
lokpal3
lokpal4
lokpal5
lokpal6
lokpal7
8103
8103
8103
8103
8103
8103
8103
0.026
0.017
0.092
0.054
0.055
0.017
0.010
Standard
Deviation
0.160
0.130
0.289
0.227
0.228
0.130
0.099
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-8. Hazare/Phase Interaction Variables
Variable
Observations
Mean
hazare1
hazare2
hazare3
hazare4
hazare5
hazare6
hazare7
8103
8103
8103
8103
8103
8103
8103
0.076
0.020
0.064
0.061
0.061
0.013
0.019
Standard
Deviation
0.264
0.140
0.245
0.240
0.240
0.112
0.138
55
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-9. Hunger Strike/Phase Interaction Variables
Variable
Observations
Mean
strike1
strike2
strike3
strike4
strike5
strike6
strike7
8103
8103
8103
8103
8103
8103
8103
0.013
0.019
0.023
0.012
0.010
0.003
0.002
Standard
Deviation
0.113
0.137
0.150
0.110
0.099
0.057
0.040
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-10. Demonstration/Phase Interaction Variables
Variable
Observations
Mean
demo1
demo2
demo3
demo4
demo5
demo6
demo7
8103
8103
8103
8103
8103
8103
8103
0.051
0.027
0.061
0.069
0.040
0.010
0.014
Standard
Deviation
0.219
0.161
0.240
0.254
0.195
0.102
0.118
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-11. Government/Phase Interaction Variables
Variable
Observations
Mean
govt1
govt2
govt3
govt4
govt5
govt6
govt7
8103
8103
8103
8103
8103
8103
8103
0.038
0.016
0.042
0.017
0.043
0.012
0.011
Standard
Deviation
0.192
0.127
0.201
0.131
0.204
0.107
0.107
Minimum
Maximum
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Table F-12. Real-World Event Variables
Variable
Observations
Mean
rwposgovact
rwneggovact
rwprotestevent
rwparlsess
8103
8103
8103
8103
0.144
0.076
0.273
0.430
56
Standard
Deviation
0.351
0.266
0.446
0.495
Minimum
Maximum
0
0
0
0
1
1
1
1
Appendix G: Predictive Regression Model
We investigated possible causal linkages between Facebook activity and realworld activity—namely government responses to protest movement demands. To
establish a causal link between Facebook activity and real-world activity, it would
be useful to demonstrate a strong relationship between key indicators of Facebook
activity in the days or weeks prior to government activity and subsequent
government action. If we find a strong temporal relationship between likes or
comments and government actions in the days preceding the government action,
the correlation in activity could support further investigative means to establish
causality (Figure G-1).
Figure G-1. Idealized Predictive and Responsive Temporal Correlations in Ln Likes,
Relative to Government Action
Odds of Observing a Government
Action Given a 1 Unit Increase in
Natural Log of Likes
4
3
3
2
2
1
-6
-5
1
-4
-3
-2
-1
0
1
2
3
4
5
6
Government Action (+/- Day Lags)
Prediction of Govt Action
Response to Govt Action
The method we employed to test temporal correlation is a basic logit model
regression with exponentiated coefficients (odds ratios). We employed
government activity as our dependent, dichotomous variable. The natural log of
likes or comments are independent variables, as are post themes, content types,
protest events, parliamentary sessions, and days of the week:
log
1−
= +
+
+
+
+
+
ℎ
+
We ran multiple variations on the model, employing single day lags on (+/- 1 to 6
days) and cumulative day lags (+/- 1 through 6 days). No results proved predictive
of government action, while odds ratios in response to negative government
action were positive and statistically significant. As such, our data does not
support causal inference.
57
Appendix H: Regression Results
This table presents the full regression results of the basic and interaction models
using the natural log of likes and the natural log of comments as the dependent
variable.
Table H-1. Regression Results. Standard errors in parentheses. Levels of statistical
significance indicated by asterisks: *** 99%; ** 95%; * 90%.
Models
Explanatory
Variables
Corruption
Lokpal
Hazare
Strike
Demonstration
Government
Video
Blog
News
Web
Photo
RW Protest Act
RW Positive Gov't
Act
RW Negative Gov't
Act
RW Parliament
Session
Page
Phase2
Phase3
Phase4
Phase5
Basic Model
(1)
Ln Likes
-0.008
(-0.016)
-0.000
(-0.016)
-0.043***
(-0.016)
0.044*
(-0.026)
-0.008
(-0.015)
-0.004
(-0.018)
0.045*
(-0.027)
0.042
(-0.034)
-0.007
(-0.021)
-0.004
(-0.017)
0.007
(-0.022)
0.077**
(-0.037)
0.141***
(-0.033)
0.020
(-0.037)
0.212***
(-0.032)
-0.467***
(-0.036)
2.467***
(-0.056)
2.401***
(-0.038)
3.391***
(-0.052)
3.236***
(-0.037)
(2)
Ln Comments
-0.009
(-0.025)
-0.022
(-0.024)
-0.042*
(-0.024)
-0.006
(-0.039)
0.033
(-0.023)
0.014
(-0.027)
0.074*
(-0.040)
0.036
(-0.052)
-0.013
(-0.032)
-0.012
(-0.025)
0.022
(-0.033)
-0.087*
(-0.049)
0.152***
(-0.043)
0.150***
(-0.049)
-0.050
(-0.042)
-0.646***
(-0.048)
1.864***
(-0.074)
1.968***
(-0.050)
2.798***
(-0.068)
2.488***
(-0.049)
58
Interaction Model
(3)
(4)
Ln Likes
Ln Comments
-0.044
-0.082
(-0.035)
(-0.055)
-0.042
0.043
(-0.047)
(-0.073)
-0.606***
-0.567***
(-0.056)
(-0.078)
0.055
0.065
(-0.065)
(-0.105)
-0.039
0.005
(-0.037)
(-0.057)
-0.005
-0.035
(-0.039)
(-0.060)
0.053**
0.079*
(-0.027)
(-0.041)
0.046
0.039
(-0.035)
(-0.052)
-0.010
-0.016
(-0.021)
(-0.032)
-0.026
-0.033
(-0.017)
(-0.026)
0.003
0.017
(-0.022)
(-0.034)
0.144***
-0.021
(-0.036)
(-0.049)
0.133***
0.142***
(-0.031)
(-0.042)
0.024
0.150***
(-0.035)
(-0.048)
0.078**
-0.160***
(-0.033)
(-0.044)
-0.455***
-0.635***
(-0.036)
(-0.048)
2.066***
1.451***
(-0.067)
(-0.094)
2.080***
1.670***
(-0.049)
(-0.069)
3.103***
2.517***
(-0.059)
(-0.082)
2.974***
2.233***
(-0.047)
(-0.066)
Models
Explanatory
Variables
Phase6
Phase7
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Basic Model
(1)
Ln Likes
3.048***
(-0.066)
3.141***
(-0.053)
0.017
(-0.039)
0.088**
(-0.040)
0.131***
(-0.040)
0.109***
(-0.040)
0.005
(-0.040)
-0.025
(-0.039)
(2)
Ln Comments
2.273***
(-0.087)
2.086***
(-0.069)
0.041
(-0.053)
0.150***
(-0.054)
0.165***
(-0.053)
0.161***
(-0.053)
-0.039
(-0.053)
-0.033
(-0.053)
Corruption2
Corruption3
Corruption4
Corruption5
Corruption6
Corruption7
Lokpal2
Lokpal3
Lokpal4
Lokpal5
Lokpal6
Lokpal7
Hazare2
Hazare3
Hazare4
Hazare5
59
Interaction Model
(3)
(4)
Ln Likes
Ln Comments
2.824***
2.125***
(-0.078)
(-0.109)
2.782***
1.778***
(-0.065)
(-0.090)
0.026
0.050
(-0.038)
(-0.052)
0.091**
0.153***
(-0.038)
(-0.053)
0.140***
0.178***
(-0.038)
(-0.051)
0.110***
0.165***
(-0.038)
(-0.052)
0.008
-0.032
(-0.038)
(-0.052)
-0.011
-0.017
(-0.038)
(-0.052)
-0.002
0.077
(-0.060)
(-0.091)
0.040
0.084
(-0.047)
(-0.072)
0.071
0.088
(-0.056)
(-0.086)
0.060
0.152*
(-0.054)
(-0.082)
-0.102
-0.142
(-0.092)
(-0.139)
0.028
-0.106
(-0.079)
(-0.119)
0.138*
0.005
(-0.078)
(-0.119)
0.130**
0.0131
(-0.055)
(-0.086)
-0.016
-0.078
(-0.058)
(-0.091)
0.003
-0.135
(-0.058)
(-0.089)
-0.004
-0.332**
(-0.088)
(-0.134)
0.151*
0.005
(-0.091)
(-0.138)
0.512***
0.558***
(-0.088)
(-0.129)
0.511***
0.511***
(-0.065)
(-0.093)
0.633***
0.544***
(-0.066)
(-0.094)
0.644***
0.659***
(-0.065)
(-0.092)
Models
Explanatory
Variables
Basic Model
(1)
Ln Likes
(2)
Ln Comments
3.764***
(-0.055)
8071
0.673
0.395
2.842***
(-0.074)
7981
0.392
0.313
Source: Authors’ calculations
Hazare6
Hazare7
HungerStrike2
HungerStrike3
HungerStrike4
HungerStrike5
HungerSrike6
HungerStrike7
Demonstration2
Demonstration3
Demonstration4
Demonstration5
Demonstration6
Demonstration7
Government2
Government3
Government4
Government5
Government6
Government7
Constant
N
R2
Rho
60
Interaction Model
(3)
(4)
Ln Likes
Ln Comments
0.654***
0.598***
(-0.093)
(-0.136)
0.690***
0.568***
(-0.081)
(-0.117)
0.117
0.077
(-0.094)
(-0.147)
0.034
-0.045
(-0.081)
(-0.128)
-0.017
-0.047
(-0.089)
(-0.139)
-0.098
-0.274*
(-0.094)
(-0.146)
-0.143
-0.413*
(-0.142)
(-0.217)
0.130
0.150
(-0.179)
(-0.271)
0.093
0.130
(-0.061)
(-0.092)
0.009
-0.006
(-0.048)
(-0.074)
0.108**
0.106
(-0.049)
(-0.075)
-0.044
-0.090
(-0.052)
(-0.080)
0.021
0.014
(-0.085)
(-0.128)
-0.012
0.094
(-0.072)
(-0.109)
-0.007
0.009
(-0.069)
(-0.105)
0.008
0.029
(-0.053)
(-0.081)
0.021
0.086
(-0.066)
(-0.100)
0.012
0.086
(-0.053)
(-0.082)
0.088
0.333**
(-0.092)
(-0.139)
-0.005
0.074
(-0.080)
(-0.121)
4.075***
3.127***
(-0.061)
(-0.084)
8071
7981
0.694
0.407
0.357
0.295
Appendix I: Diagnostic Tests and Limitations of the Models
To ensure that these models provide an accurate analysis of the importance
of each of these variables, we need to verify that the models do not violate any
of the key assumptions of linear regression modeling and to try to correct for
them if they do. We associated several significant problems with running these
regressions using regular ordinary least squares (OLS). First, both the BreuschPage/Cook-Weisberg test and the White Test find that these models are
heteroscedastic, meaning the error terms do not have the same variance. This
heteroscedasticity can bias the standard errors of the coefficients and result in
incorrect conclusions about the significance of their impact on the dependent
variable. Autocorrelation also appeared to be a problem with our models. The
Durbin-Watson d-statistic is less than one in all of the models, which suggests
it is a significant problem in all of them. The Breusch-Godfrey test for
autocorrelation confirms this issue. This problem should not influence the
coefficients but does affect the standard errors of our variables.
To correct for these two issues, we used a Praise-Winsten OLS regression with
panel-corrected standard errors with a first order autoregressive process. This
regression method uses a weighting scheme to reduce the heteroscedasticity
problem in the model that is resulting from having different panels (in this case
two different pages) of data in our regression model.144 This weight tries to
correct for the cross-sectional variance and the time-series autocorrelation.145
A reverse causality problem may exist between the natural log of likes and the
natural log of comments and some of the independent variables, especially the
RWProtestEvent variable. This problem is because not only do real protests on
the ground seem to increase the rate of Facebook interaction, but it is also
possible that Facebook posts are helping to spread the word about the protests and
to encourage people to go and participate in them. If this relationship is the case,
then these independent variables based on real-world events would be endogenous
rather than exogenous. This issue may require using two-stage least squares
regression techniques to correct.
61
Appendix J: Methodological Ideals and Realities
The methodology we developed provided the data needed for this analysis, but a
number of considerations must be kept in mind when utilizing social media data
in such a context.
Privacy Concerns
Concerns about privacy are significant issues for any project that involves
observing the content of social media sites, especially for reasons not directly
related to national security. The general public likely would not see an analysis
of public opinion or Internet activity associated with social movements as a way
to reduce a national security threat. Instead, the public may see such an analysis
as a threat to privacy and free speech rights. An example of the public scrutiny
that may accompany monitoring social media sites for reasons other than national
security threats occurred in January 2012. Disclosed information showed that a
contractor paid by the U.S. Department of Homeland Security monitored social
media sites to gauge public opinion in Standish, Michigan, to a proposal to move
detainees held in Guantanamo Bay, Cuba, to a local prison. Homeland Security
officials disputed whether the department’s Social Networking/Media Capability
program tracked public opinion. Advocacy groups such as the Electronic Privacy
Information Center decried the program as having no legal foundation and
contradicting the First Amendment.146
Cell Phones
Cell phone activity, particularly text messaging, is another valuable indicator of
social movement activity. Organizers use text messages as a way to disseminate
information and issue calls to action in a relatively short period of time. In certain
countries, cell phones may be more economically accessible to the public than the
Internet, and the population of cell phone users may therefore be much higher
than the population of Internet users.
A recent issue in the United States has been the ability of government to monitor
cell phone activity. The extent of this controversy has largely centered on the
government using location information from cell phones, but monitoring the
content of cell phone texts would likely encounter similar opposition and
questions of constitutionality.147
Twitter
Ideally, we would have analyzed Facebook and Twitter, as both social media sites
have experienced significant growths in popularity since the mid-2000s. Twitter
might have provided more of an up-to-the-minute account of the 2011 Indian
anticorruption movement’s major events. However, with the methodology
developed to gather data from Facebook, tweets posted in earlier parts of the 2011
anticorruption movement were not accessible, which is problematic. At this stage,
62
we are unable to access the necessary tweets to collect data to compare to
Facebook activity.
Computer System Requirements
Searching through Facebook posts challenged us from a technological perspective
because of the amount of memory required for a typical consumer-oriented
computer system to view posts, particularly those posted over one year ago. Even
in an age where computers are equipped with much faster processors and greater
available memory than ever before, computers sold to the general public are
typically not equipped to handle this sort of task in a reliable and relatively quick
manner. Desktop computers may be able to handle the processing and memory
requirements, but many students opt for laptops due to greater portability. Finally,
the purchase of more powerful computers has significant financial constraints.
Unless these machines are dedicated to monitoring social media sites and posts, it
may be difficult to justify such purchases.
Demographic Information
Ideally, we would access pages owned by individuals following Anna Hazare or
India Against Corruption on Facebook. However, privacy settings largely derail
efforts to collect comprehensive demographic information in an analysis of the
2011-12 Indian anticorruption movement. Privacy settings on individual accounts
can vary in strictness. Settings can range from allowing non-friends to view only
the account holder’s name and profile picture to viewing all information and posts
on the profile page.
Discrepancies in personal information disclosure can also significantly limit
collection of demographic information. Some Facebook users post more personal
information on their profiles than others; therefore, information about location,
age, and gender may be harder to obtain on some pages than others.
Language
A significant barrier associated with a number of Facebook posts is language.
Posts that are published in Hindi characters or are phonetically written in Hindi
using English characters may contain valuable information for this analysis.
However, with no team members able to understand Hindi, we were not able to
extract data from those posts. The number of top-level posts written in Hindi was
higher for the Anna Hazare Facebook page than for the India Against Corruption
Facebook page, but the language barrier limited our attempt to analyze reactions
from general Indian Facebook users.
Switch to Timeline
Since September 2011, Facebook has been moving to Timeline, which is a format
for profile pages that allows users to tell their life stories with Facebook, as
opposed to their Facebook pages merely being logs of their social networking
63
activities.148 We collected our data before the AH and IAC pages changed to
Timeline, but the new format may impede future collection of data from
Facebook posts for similar analyses. Data collection may be most hampered
by the new page layout, which changed from a linear presentation of Facebook
activity in the chronological order the posts were made to a sequential layout
based on the user’s life story. Posts of similar time periods are clustered in
Timeline, rather than appearing as a list, which may require substantially more
work, in terms of arranging posts in order of their chronological origin for coding.
64
Appendix K: Alternative Means and Methodologies
Three principle constraints limited our ability to gather social media data for this
analysis: ethical considerations for Institutional Review Board-exempted data,
technical skill set limitations, and funding. A brief discussion of these limitations,
unconstrained approaches to data collection, and current and proposed alternative
data resources follow.
Ethical Considerations
The University of Wisconsin requires Institutional Review Board approval for
research involving human subjects where researchers acquire data through
interaction or intervention or include private information.149 Facebook data,
depending on its source and location, may or may not qualify as human subjects
data. To avoid approaching the human subjects data threshold, we limited
ourselves to data publicly viewable by any Facebook user. Our collection efforts
involved the top-level posts on the two pages of interest—essentially a public
relations outlet for the India Against Corruption organization and an Anna Hazare
fan club. Furthermore, we made no attempt to collect, catalogue, investigate, or
track the user account identities of individuals who liked or commented on any
post.
To develop a more complete picture of the universe of social media activity
surrounding a real-world movement, however, we would need to engage in a
deeper exploration of the individuals associating with the movement through the
full array of social media sources or outlets available and utilized by movement
followers. Because Facebook requires users to create accounts and because the act
of liking or commenting on a post links the liker’s or commenter’s identity with
that action on the post, researchers may be able to fully catalogue an individual
user account’s actions with respect to a given social movement and capture the
entire gamut of a user’s public Facebook actions. Depending upon the user’s
configuration of account security settings, researchers may be able to track all
liking or commenting individuals to their personal walls and catalogue their
walls’ posts, unrelated like and comment activities, friend networks, photo
albums, and demographic and behavioral information explicit in the profile details
and implicit in user photo albums, pages liked, etc. Because top-level post likes
and comments are merely an indirect and incomplete measure of the significance
of social media in real-world social movements, exploring the activities of
individuals (e.g., rally attendance documented by self-published photographs,
reposts of others’ content, sharing with friends, etc.) through social media data is
an ideal means of determining virtual and real-world activity correlations. With
this information researchers may be able to profile, in relative and absolute terms,
an individual’s participation, contribution, and importance to a movement through
the social media framework.
65
Technical and Budgetary Limitations
To accomplish this type of multi-level data collection, our manual “screen
scraping” methodology is unsuitable. Automated “web spider” programs that
systematically identify, explore, and catalogue all links, content, and
interconnections could be used. Such methods typically violate social networking
terms of use agreements due to the excessive hardware resource requirements
imposed by “robot” crawlers that simultaneously call upon multiple web pages
and linked content. Alternative collection approaches using the built-in
Application Programming Interface are available, robust, and explicitly
authorized by terms of use, but require specialized programming knowledge to
design queries appropriate to the provided interfaces. Our team did not possess
these programming skills, nor was it feasible to acquire them during the project
period. If future research is funded and research team members lack these
abilities, skilled programmers can be hired to develop keyword and contentfocused site-specific tools.
Making Friends
The web spider and Application Programming Interface approaches are
themselves limited in their ability to fully explore social networks by the privacy
and account settings afforded to individual users. If a Facebook user only allows
their personal page’s content to be viewed by friends, then spider or Application
Programming Interface queries would only catalogue a minimal profile page with
user account name, profile photo, and other minimal demographic characteristics
permitted by the user. To map the full network of individuals following and
participating in a social media movement, researchers must befriend those
individuals to gain access to their full profiles, photo albums, wall postings, etc.
Generating favorable online identities and using those identities to pursue
friendships is one plausible means to this end. Based on our experience as
Facebook users, manufactured identities and friendships are far more likely to be
successful if the individuals of interest already have a high number of friends
associated with their account (250+). The greater number of friends makes it more
likely that a plausible acquaintance or “kindred spirit” would be accepted into a
friend network.
Alternative Data Sources
The Director of National Intelligence Open Source Center purports to catalogue
and host Internet and other open source content “from more than 160 countries, in
more than 80 languages and hosts content from several commercial providers, as
well as content from Open Source Center partners.”150 Access to the center
requires an official U.S. government purpose. For this reason we do not know and
cannot verify if the center catalogues foreign social media activity, but it seems to
be a natural home for aggregation of open source, publicly available information
of this nature. A January 2012 news media report suggests these data are being
collected and utilized.151
66
Analytical Toolkits
The Federal Bureau of Investigation has published a request for information to
“determine the capability of industry to provide an Open Source and social media
alert, mapping, and analysis application solution.”152 The request requires the
applicant to “have the ability to rapidly assemble critical open source information
and intelligence that will allow [the Strategic Information and Operations Center]
to quickly vet, identify, and geo-locate breaking events, incidents and emerging
threats.”153 If such a tool is proposed for bid, developed, and implemented, it
would greatly facilitate the data collection and analysis methods discussed above
and make obsolete our manual approach to content identification and impact
tracking.
67
Endnotes
1
Chang, “Like My Status: Memology 2011.”
2
@twitter, “#numbers.”
3
Facebook, “Key Facts.”
4
Ellison, Steinfield and Lampe, “Benefits of Facebook ‘Friends,’” 1145.
5
Ellison, Steinfield and Lampe, “Benefits of Facebook ‘Friends,’” 1146.
6
Zhang et al., “Revolution Will Be Networked,” 79.
7
Stein, “Social Movement Web Use,” 750
8
Stein, “Social Movement Web Use,” 753-754.
9
Stein, “Social Movement Web Use,” 754-755.
10
Sheedy, Social Media for Social Change, 7.
11
Papic and Noonan, “Social Media as a Tool for Protest.”
12
MacKinnon, “Flatter World and Thicker Walls.”
13
Allan and Brown, “Mavi Marmara.”
On May 31, 2010, Israeli commandos attacked a Turkish aid ship called the Mavi Marmara bound
for Gaza. Videos of the attack made from confiscated footage, CCTV cameras, and Israel Defence
Forces’ surveillance were uploaded to YouTube by Israel’s official public relations and media
body, while professional journalists and pro-Palestine activists who were onboard were detained
for a number of days and unable to report.
14
Transparency International, Global Corruption Barometer, 25.
15
Government of India Ministry of Law and Justice. "The Right to Information Act."
16
“Corruption in India: A Million Rupees Now.”
Officials of the 2010 Commonwealth Games have been accused of corruption and
mismanagement, and the Indian Government has formed a committee to investigate these
accusations. The Games were plagued by problems such as infrastructure issues and
overspending. Officials of the Indian Premier League have been accused of tax evasion and graft,
clouding the reputation of the 4-year-old league. Government officials undercharged telecom
companies for licenses in the 2G spectrum scam, leading to arrests of politicians, corporate
executives, bureaucrats, and corporations.
17
Tharoor, “India's Anti-Corruption Contest.”
18
Jain, “Dharma Does Not Live Here,” 1591.
19
Ministry of Personnel, Public Grievances and Pensions, “Government Accepts
Recommendations.”
20
Sawyer and Sawyer, “The Amazing Rise of Anna Hazare.”
21
AnnaHazare.org, Biography.”
22
“Protests in India - Jail the Messenger.”
23
Prime Minister's Office, “Appeal to Anna Hazare.”
24
“India Activist Anna Hazare Ends Hunger Strike.”
25
Khorana and Harindranath, “New Technologies, Gandhian Activism,” 3-5.
26
Joint Drafting Committee, Minutes of the Fifth Meeting.
68
27
Mukerji, “A Tale of Three Fasts,” 28-32; Joint Drafting Committee, Minutes of the Sixth
Meeting.
28
Joint Drafting Committee, Minutes of the Ninth Meeting.
29
Prime Minister's Office, “Prime Minister’s Opening Remarks.”
30
Prime Minister’s Office, “PM’s Opening Remarks at the All Party Meeting”; Ministry of
Personnel, Public Grievances and Pension, “Cabinet Approves the Lokpal.”
31
Vasudevan, “Fighting Corruption.”
32
Economic Times Bureau, “Anna Hazare Denied Permission.”
33
Benedict, “Edgy Congress.”
34
Mukerji, “A Tale of Three Fasts,” 28-32.
35
“Protests in India - Jail the Messenger.”
36
De Bendern and Scrutton, “Police Order Anna Hazare Freed”; Mukerji, “A Tale of Three
Fasts,” 28-32.
37
Magnier, “Indian Activist Anna Hazare Ends Standoff with Government.”
38
Benedict, “Edgy Congress.”
39
Press Trust of India, “Parliament Adopts ‘Sense of House.’”
40
“Protests in India - The Fast and the Curious.”
41
Bahree and Agarwal, “India Moves to Toughen Fight.”
42
Tharoor, “India's Anti- Corruption Contest.”
43
Press Trust of India, “Hazare Discharged from Hospital.”
44
Khorana and Harindranath, “New Technologies, Gandhian Activism,” 3.
45
Kurup, “How Web 2.0 Responded to Hazare.”
46
India Against Corruption Facebook Page.
47
Google Play.
48
Freedom House, “Freedom on the Net 2011,” 25.
49
Freedom House, “Freedom on the Net 2011,” 169.
50
Sharma and Vascellaro, “Google and India Test the Limits.”
51
Freedom House, “Freedom on the Net 2011,” 167.
52
Press Trust of India, “Kapil Sibal’s Web Censorship.”
53
World Bank, Internet Users Per 100 People.
54
Freedom House, “Freedom on the Net 2011,” 161; Quah, “Curbing Corruption in India,” 255.
55
Socialbakers.com, India Facebook Statistics.
56
Socialbakers.com, India Facebook Statistics.
57
Socialbakers.com, India Facebook Statistics.
58
Indo-Asian News Service, “Anna Hazare to Go on Fast as Scheduled.”
59
India Against Corruption Facebook Page.
60
India Against Corruption Facebook Page.
69
61
Press Trust of India, “PMO Expresses Disappointment.”
62
Anna Hazare Facebook Page 2012.
63
India Against Corruption Facebook Page.
64
PRS Legislative Research, Parliament Updates.
65
Desai, “Social Activists Draft New Lokpal Bill.”
66
Ministry of Personnel, Public Grievances and Pensions, “Government Accepts
Recommendations.”
67
“Anna Hazare’s Crusade against Corruption: A Timeline.”
68
“Anna Hazare’s Crusade against Corruption: A Timeline.”
69
Prime Minister's Office, “Appeal to Anna Hazare.”
70
Prime Minister's Office, “Appeal to Anna Hazare.”
71
“Anna Hazare on Hunger Strike against Corruption.”
72
“India Activist Anna Hazare Ends Hunger Strike.”
73
Ministry of Law and Justice, “Government Issues Notification.”
74
“Anna Hazare’s Team Launched Website.”
75
Joint Drafting Committee, Minutes of the First Meeting.
76
Joint Drafting Committee, Minutes of the Second Meeting.
77
Joint Drafting Committee, Minutes of the Third Meeting.
78
Ministry of External Affairs, “India Ratify UN Conventions.
79
Joint Drafting Committee, Minutes of the Fourth Meeting.
80
Joint Drafting Committee, Minutes of the Fifth Meeting.
81
Mukerji, “A Tale of Three Fasts,” 28-32.
82
Mukerji, “A Tale of Three Fasts,” 28-32.
83
Mukerji, “A Tale of Three Fasts,” 28-32.
84
Mukerji, “A Tale of Three Fasts,” 28-32.
85
Joint Drafting Committee, Minutes of the Sixth Meeting.
86
Mukerji, “A Tale of Three Fasts,” 28-32.
87
Joint Drafting Committee, Minutes of the Seventh Meeting.
88
SME News, “Anna Hazare Announces Another Hunger Strike.”
89
Joint Drafting Committee, Minutes of the Eighth Meeting.
90
Joint Drafting Committee, Minutes of the Ninth Meeting.
91
Prime Minister’s Office, “All Party Resolution on Lokpal Bill.”
92
Agencies, “Hazare Discharged in Funds.”
93
Ministry of Personnel, Public Grievances and Pensions, “Cabinet Approves the Lokpal Bill.”
94
Vasudevan, “Fighting Corruption.”
95
Economic Times Bureau, “Anna Hazare Denied Permission.”
70
96
Press Trust of India, “PIL Filed in Supreme Court.”
97
Times News Network, “Protest against Govt-Drafted Lokpal Bill.”
98
Prime Minister’s Office, “PM’s Opening Remarks at All Party Meeting.”
99
Times News Network, “Lokpal Bill Tabled in Lok Sabha.”
100
Prime Minister’s Office, “Prime Minister’s Reply.”
101
Benedict, “Edgy Congress.”
102
Mukerji, “A Tale of Three Fasts,” 28-32.
103
Times News Network, “Anna Hazare Arrested.”
104
Agencies, “Released, Anna Stays Put.”
105
Magnier, “Indian Activist Anna Hazare Ends Standoff with Government.”
106
Benedict, “Edgy Congress.”
107
“India Corruption: Anna Hazare Leaves Jail.”
108
Prime Minister’s Office, “Prime Minister Writes.”
109
Prime Minister’s Office, “PM’s Opening Remarks.”
110
Prime Minister’s Office, “All Party Resolution on Lokpal Bill.”
111
“Anna Hazare’s Crusade against Corruption: A Timeline.”
112
Prime Minister’s Office, “PM’s Letter to Anna Hazare.”
113
Agencies, “Anna Hazare Ends Fast on Day 13.”
114
Press Trust of India, “IT Notice”; Agencies, “IB Harassing My Kin.”
115
Parsai, “Prashant Bhushan.”
116
Ministry of Personnel, Public Grievances and Pensions, “Government Accepts
Recommendations.”
117
Ministry of Personnel, Public Grievances and Pensions, “President to Inaugurate.”
118
Staff Reporter, “Hazare on Vow of Silence.”
119
Times News Network, “Congress Beaten 0-4 in Bypolls”; “Hazare Warns Congress.”
120
Indo-Asian News Service, “Anna Hazare Ends ‘maun vrat.’”
121
Gupta, “Standing Panel to Adopt Lokpal Bill”; Dhawan , “Lokpal Panel Leaves PM Issue.”
122
Times News Network, “Lokpal Bill Report Tabled”; Indo-Asian News Service, “Team Anna
Urges People to Protest.”
123
Special Correspondent, “Manmohan Calls All-Party Meet.”
124
Press Trust of India, “Anna Sits on Third Fast”; Sinha, “At 73, Fasting Not an Option.”
125
Indo-Asian News Service, “Government Okays Key Anti-Graft Bills.”
126
Times News Network, “Anna Hazare Announces ‘jail bharo.’”
127
Ministry of Personnel, Public Grievances and Pensions, “Lokpal and Lokayuktas Bill.”
128
Fontanella-Khan and Crabtree, “Hazare Begins Mumbai Hunger Strike.”
129
Indo-Asian News Service, “Lok Sabha Passes Lokpal Bill.”
130
Times News Network, “Anna Hazare to Break Fast.”
71
131
Press Trust of India, “Rajya Sabha Adjourned Sine Die.”
132
Express News Service, “Anna Hazare’s Condition Improves”; Times News Network, “Tour in
Poll-Bound States.”
133
Press Trust of India, “Hazare Discharged from Hospital.”
134
Times News Network, “Team Anna to Start Campaign.”
135
Times News Network, “Team Anna to Start Campaign.”
136
Times News Network, “Anna to Rest.”
137
Facebook, “About.”
138
Gupta, “Standing Panel to Adopt Lokpal Bill.”
139
Press Trust of India, “Congress Declines to Disclose Stand.”
140
For this section, we obtained averages by summing total likes and comments per day across the
Anna Hazare and India Against Corruption pages and averaging the total for the 389-day period.
141
Special Correspondent, “It Won’t Work.”
142
Timmons, “India Asks Google.”
143
All times listed are in Central Standard Time.
144
Bailey and Katz, “Implementing Panel-Corrected.”
145
Monogan, “Pooling Space and Time.”
146
Savage, “Federal Contractor Monitored.”
147
Richtel, “Is the Government Tracking Us.”
148
Lessin, “Tell Your Story.”
149
University of Wisconsin-Madison Health Sciences Institutional Review Boards, Protocol
Closure Guidance.
150
Open Source Center.
151
Martin, “CIA Tracks Public Information.”
152
U.S. Department of Justice, Federal Bureau of Investigation, “Social Media Application.”
153
U.S. Department of Justice, Federal Bureau of Investigation, “Social Media Application.”
72
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