SC317 Social Media and Social Research
T/Th 4:30-5:45
Gasson 207
Instructor: Margaret Willis
Email: margaret.willis@bc.edu
Office Hours: Tuesdays 2-3 in McGuinn 410B, and by appointment
Course Description
Social media sites such as Facebook and Twitter, and search engines like Google, have
become indispensable in our daily lives. Much of what we do on these sites generates large
amounts of data: what we search for, what we “like,” whom we “follow.” Marketers,
journalists, and researchers analyze these data for many different purposes and interests.
In this course, we will use a sociological perspective to examine what these new forms of
data are, how they are produced through our actions online, and how these data are then
used, sometimes in questionable ways. Prerequisites: some familiarity with the use of data
in social inquiry, as introduced in SC200 (Statistics) and SC210 (Research Methods).
Course Objectives
This course will help you to:
- Examine the social processes of data production and interpretation, including race,
class, and gender dynamics
- Understand what kind of data are generated when we use social media sites or
Google
- Strengthen your skills for critically evaluating social research
- Sharpen and extend skills introduced in SC200 and SC210
- Apply sociological analysis to an emergent and pervasive social phenomenon that
you are likely to encounter in your career and/or in your everyday life
- Build research, writing, and data analysis skills through an extended, hands-on
research project
Required Texts
Gitelman, Lisa. 2013. “Raw Data” is an Oxymoron. MIT Press.
All other readings available on Blackboard
Course Requirements
30 points
30 points
15 points
5 points
20 points
Six response papers/participation
Midterm (in-class, short essay responses)
Research project proposal (4-5 pages)
Brief in-class presentation of research findings
Final research project (8-10 pages, including tables/graphs)
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1. Six Response Papers & Participation (30 points)—There will be eight response
paper options. You need to complete and submit six response papers over the course of
the semester. Each response that you complete will be worth up to five points. Prompts
for each response paper will be on Blackboard in the “Assignments” section. For each
response paper, answer all of the questions fully but concisely. Your responses should
be approximately 500 words. When citing ideas from our class materials, use in text,
parenthetical citations like this: (Author last name, year). Include page numbers for any
“direct quotes” (Author, 2013, p. 3). Post your responses on Blackboard by Thursday at
4:30PM to receive credit for that week; late responses will not be accepted.
Participation in class and good attendance is expected. You get three “free” absences
for illnesses, etc. For every absence beyond those three you will lose one point (that
translates to one point off your final grade). Please contact me about extenuating
circumstances (substantial illnesses with a note from a doctor, family emergencies) as
soon as possible.
2. Midterm (30 points)—The in-class midterm is scheduled for February 27th. The
format will include short essay responses about material covered in the first portion of
the course.
3. Research Proposal (15 points)—A detailed (4-5 page) proposal for your final research
proposal will be due on Sunday April 6th at midnight on Blackboard. Guidelines for the
final project and the research proposal will be distributed and discussed in class.
4. Brief Presentation (5 points)—You will share a very brief presentation of your final
research project during the final week of class, April 29th or May 1st.
5. Final Research Project (20 points)—Final research project reports (8-10 pages,
including tables/graphs) will be due electronically by Tuesday, May 6th, by 6PM. This
project will require you to articulate a research question and use one of the publicly
available interfaces to work with Google or Twitter data.
Grade Scale
A
AB+
B
BC+
C
CD+
D
DF
94-100
90-93
87-89
84-86
80-83
77-79
74-76
70-73
67-69
64-66
60-63
<60
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Course Policies
Technology: You are welcome to use laptops or tablets for only three purposes in class:
taking notes, looking at an electronic copy of a course reading, or following along in class
exercises. No email or other distractions. Please silence and store all cell phones.
Class etiquette: Arrive on time. Come with a paper or electronic copy of all of the day’s
readings. Do all required readings before class and come prepared to discuss. No food, but
drinks are fine.
Late work: Late response papers will not be accepted. In the event of extenuating
circumstances (substantial illness, family emergency), you must arrange any extensions for
other assignments with me as soon as possible. If no arrangement has been made, your
grade for the assignment will decrease by one point for each day it is late.
Academic integrity: I expect that all work that you submit will be your own work. You must
cite any published work that you use in your writing. Plagiarism will lead to a failure for the
assignment. Please see me with any questions regarding citations or collaborations, and
also familiarize yourself with Boston College’s academic integrity policy:
www.bc.edu/offices/stserv/academic/resources/policy/#integrity.
Accommodations: If you are a student with a documented disability seeking reasonable
accommodations in this course, please contact Kathy Duggan, dugganka@bc.edu, 617-5528093, at the Connors Family Learning Center regarding learning disabilities and ADHD, or
Paulette Durrett, (617) 552-3470, paulette.durrett@bc.edu, in the Disability Services Office
regarding all other types of disabilities, including temporary disabilities. Advance notice
and appropriate documentation are required for accommodations.
Schedule of Topics and Assignments
TOPIC
Week 1:
Introduction—The
rise of Big Data
Week 2: A brief
history
DATE ASSIGNMENTS
1/14 Introduction
1/16 1. Marwick. 2014. How Your Data Are Being Deeply
Mined. The New York Review of Books.
2. Marcus. 2013. Steamrolled by Big Data. The New
Yorker.
3. Lazer et al. 2009. Computational Social Science.
Science, 323:721-23.
1/21 1. McChesney. 2013. Ch. 4. Digital Disconnect.
2. Levy. 2011. Ch. 1. The Plex.
1/23 1. McChesney. 2013. Ch. 5. Digital Disconnect.
2. Bilton. 2013. All is Fair in Love and Twitter. The New
York Times Magazine.
Response 1 due on Blackboard by class time
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Week 3: Google, Social
Media, and daily life
1/28
1/30
Week 4: Data,
methods, and theory
2/4
Week 5: The research
sample
2/11
2/6
2/13
Week 6: Coding and
interpreting
2/18
2/20
Week 7:
Appropriation and
meaning; MIDTERM
2/25
1. Stephens. 2011. Thinking Through Moving Media.
Social Research, 78: 1133-54.
2. Vaidhyanathan. 2011. Ch. 6 and Conclusion. The
Googlization of Everything.
1. boyd. 2011. Why Youth (Heart) Social Network Sites.
Gender, Race, and Class in Media.
2. boyd. 2012. Participating in the Always-On Lifestyle.
The Social Media Reader.
3. Murthy. 2012. Toward a Sociological Understanding of
Social Media: Theorizing Twitter. Sociology, 46(6):
1059-73.
Response 2 due on Blackboard by class time
1. Gitelman and Jackson. Introduction. Raw Data p.1-14.
2. Rosenberg. Data before the Fact. Raw Data p. 15-40.
1. Anderson. 2008. The End of Theory. Wired.
2. Bowker. Afterword. Raw Data p. 167-71.
3. Latour. 1987. Ch. 1. Science In Action.
Response 3 due on Blackboard by class time
1. Huck et al. 2010. Sample. Encyclopedia of Research
Deign. Sage.
2. Acheson. 2010. Sample Size. Encyclopedia of Research
Design. Sage.
3. Pew Research Reports:
2013: Internet Use in the U.S.
2012: Search Engine Use in the U.S.
2013: Social Network/Social Media Use in the U.S.
4. Graham. 2012. Digital Divide: The Geography of
Internet Access. Environment & Planning, 44:1009-10.
1. boyd and Crawford. 2012. Critical Questions for Big
Data. Information, Communication, & Society, 15:662-79.
Response 4 due on Blackboard by class time
1. Marcus. 2013. Why Can’t My Computer Understand
Me? New Yorker.
2. Zappavinga. 2011. Ambient Affiliation: A Linguistic
Perspective on Twitter. SAGE Internet Research
Methods.
3. Michel et al. 2011. Quantitative Analysis of Culture
Using Millions of Digitized Books. Science, 331:176-82.
1. Star and Bowker. 1999. Ch 1. Sorting Things Out.
2. Stanley. Where Is That Moon, Anyway? Raw Data p.7788.
Response 5 due on Blackboard by class time
1. Garvey. “facts and FACTS”: Abolitionists’ Database
Innovations. Raw Data p. 89-102.
2. Coleman. 2012. Phreaks, Hackers, and Trolls. The Social
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SPRING BREAK
SPRING BREAK
Week 8: Data
acquisition, data
mining
Week 9: Evaluating
research claims
2/27
3/4
3/6
3/11
3/13
3/18
3/20
Week 10:
Infrastructure and
labor
3/25
Week 11: LAB—using
data interfaces
4/1
Week 12: Surveillance
and Research Ethics
4/8
3/27
4/3
Media Reader.
In-class Midterm
SKIM (you won’t be responsible for the technical details):
1. Russell. 2013. Ch 1. Mining the Social Web.
1. Furnas. 2012. Everything You Wanted to Know About
Data Mining but Were Afraid to Ask. The Atlantic.
2. Little & Schucking. 2008. Data Mining, Statistical Data
Analysis, or Advanced Analytics. SAGE Handbook of
Online Research Methods.
3. Leinweber. 2007. Stupid Data Miner Tricks. The Journal
of Investing, 15-22.
1. Golder & Macy. 2011. Diurnal and Seasonal Mood Vary
with Work, Sleep, and Daylength Across Diverse
Cultures. Science, 333: 1878-81.
2. Ginsberg et al. 2009. Detecting Influenza Epidemics
Using Search Engine Query Data. Nature, 457:1012-14.
3. Reis & Brownstein. 2010. Measuring the Impact of
Health Policies Using Internet search Patterns: The
Case of Abortion. BMC Public Health, 10:514-9.
1. Stephens-Davidowitz. 2013. Dr. Google Will See You
Now. New York Times.
2. Ayers et al. Seasonality in Seeking Mental Health
Information on Google. Am J Prev Med (2013)
Response 6 due on Blackboard by class time
1. Vanderbilt. 2009. Data Center Overload. New York
Times.
2. Ribes and Jackson. Data Bite Man. Raw Data p.147-66.
1. Miller. 2013. Curtain is Rising on a Tech Premiere With
(as Usual) a Mostly Male Cast. The New York Times.
2. Fuchs. 2012. The Political Economy of Privacy on
Facebook. Television & New Media, 13(2):139-159.
Response 7 due on Blackboard by class time
Bring laptop OR meet in computer lab: TBA
Will work with Google tools in class
Bring laptop OR meet in computer lab: TBA
Will work with Twitter tools in Excel in class
Research Proposal DUE Sunday 4/6 by Midnight,
Blackboard
1. Eynon et al. 2008. The Ethics of Internet Research. The
SAGE Handbook of Online Research Methods.
2. Bossewitch & Sinnreich. 2013. The End of Forgetting:
Strategic Agency Beyond the Panopticon. New Media
Society, 15:224-42.
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4/10
Week 13:
Case Study=
Marketing
4/15
EASTER BREAK
Week 14:
LAB and
Case Study= Medicine
4/17
4/22
Week 15:
Presentations
FINAL
4/29
5/1
5/6
4/24
1. Raley. Dataveillance and Countervailance. Raw Data p.
121-45.
2. Fuchs et al. 2012. Introduction. Internet and
Surveillance: The Challenges of Web 2.0 and Social
Media.
Response 8 due on Blackboard by class time
1. Piskorski. 2011. Social Strategies that Work. Harvard
Business Review.
2. Anderson. 2012. The Long Tail. Social Media Reader.
3. Segal. 2013. Riding the Hashtag in Social Media
Marketing. The New York Times.
Bring laptop OR meet in computer lab: TBA
Come prepared to work on you data analysis; in-class
trouble shooting
1. Flores et al. 2013. P4 Medicine: How Systems Medicine
Will Transform the Healthcare Sector and Society.
Personalized Medicine, 10(6):565-76.
2. O’Connor. 2013. The Apomediated World: Regulating
Research When Social Media Has Changed Research.
Journal of Law, Medicine & Ethics, 470-83.
3. Bowden. 2012. The Measured Man. The Atlantic.
4. Wolf. 2010. The Data-Driven Life. The New York Times.
In-class presentations
In-class presentations
FINAL DUE electronically by 6PM
ADVANCED RESOURCES about social media data mining and data science, for the curious:
Online:
UCLA Intro to Digital Humanities: http://dh101.humanities.ucla.edu/
UC Berkeley School of Information Course: Analyzing Big Data with Twitter:
http://www.youtube.com/playlist?list=PLE8C1256A28C1487F
Twitter’s Official “Twitter University”: http://www.youtube.com/twitteruniversity
Big Data University: http://bigdatauniversity.com/
Books:
The O’Reilly series: http://shop.oreilly.com/category/get/data-science-kit.do
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