Sociotechnical Behavior Mining: From Data to Decisions? Papers from the 2015 AAAI Spring Symposium
Analyzing Flash Mobs in Cybernetic Space and the Imminent Security Threats
A Collective Action Based Theoretical Perspective on Emerging Sociotechnical Behaviors
Samer Al-khateeb and Nitin Agarwal
Department of Information Science
University of Arkansas at Little Rock
2801 S University Ave, Little Rock, AR 72204
Email: {sxalkhateeb, nxagarwal}@ualr.edu
walls and continuously search for new ways to challenge
the rules of gravity. Parkour history is more than hundred
years as Michael Atkinson states in his article “Parkour,
Anarcho-Environmentalism, and Poiesis” (Atkinson 2009).
This practice goes back to a style called Hebertism that
emerged at the beginning of the 20th century. The practice
contains variety of obstacles and landscapes usually in the
wooded setting “as an unfettered animal” (Mohilever 2012).
The first parkour was formed originally in Paris and since
then it emerged all over the world, from Canada and USA
to Russia and the Philippines. Due to a smaller number of
participants involved in the act, parkour has not lead to as
big security threats as flash mobs have. Also, parkour nature
was initiated as a postsport lifestyle and then it went through
a “sportization process” as Atkinson concludes. Sportization is the process by which subaltern or alternative forms
of sport, leisure and play are co-opted and incorporated into
conventional sports cultures (Atkinson 2009).
Flash mobs (FMs) is another form of public engagement
that is more commonly observed than parkour. Oxford English dictionary defines a flash mob as “A large public gathering at which people perform an unusual or seemingly random act and then quickly disperse” (Oxford-Dictionary July
8 2004). Recent observations pertaining to the deviant aspect of the flash mobs have insisted to add a highly debated
perspective, which is the nature of the flash mob (whether it
is for entertainment, satire, and artistic expression or is it a
deviant act that can lead to robberies and thefts i.e. criminal flash mobs). Flash mobs could be entertaining such
as, Happy Birthday for a bus driver (Kirkland December
21 2011) or they can be the new face of transnational crime
organizations (TCOs) that can pose significant risks to civil,
political, social, and economic stability (Ackerman and others 2013) such as, the Bash Mob that happened in Long
Beach, California on July 9, 2013 when over 100 individual who have been recruited using social media site participated in the Pike and City Place area and committed a series
of thefts, property damage, and assaults (Holbrook July 19
2013).
The new ways of communications such as the Internet,
social media, and the mobile phones are widely responsible for the increasingly common occurrences of these practices. The new communication media made the process of
recruiting and training a specific sector of the society (spe-
Abstract
Since the occurrence of the first ‘flash mob’ organized by Bill
Wasik (senior editor of the Harper’s Magazine) in Manhattan in 2003, flash mob phenomenon has become widespread.
Recent journalistic accounts have reported that this form of
public engagement can pose significant threats to civil, political, social, and economic stability of a region. Gaps in
the scientific understanding of such phenomenon and the imminent security risks posed by such acts call for a need to
systematically study them. In this ongoing research, we shed
light on the social dynamics of the flash mob phenomenon
and build a conceptual model examining the necessary factors for the formation of flash mob and predicting its success
or failure. Grounded in the sociological theories of collective
action and collective identity formation, we evaluate the motivations of a flash mob practitioner and logically analyze the
choices he/she would face with regards to acting or withdrawing from the flash mob. More broadly, this work is an attempt
to bridge social and computational sciences that would help
clarify and explain manifestations of emerging sociotechnical
behaviors such as parkour, campaigns, and social movements
that are widely observed.
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Introduction
In the last decade and a half there has been an increasing
interest in the public participation-led guerrilla acts. Many
new forms of public engagement have been observed such
as: parkours, flash mobs, campaigns, and social movements
(see Figure 1). New technologies and the variety of affordable and easy to use means of communications (such as social network platforms, viral e-mails, and SMS) increased
the occurrences of such emerging sociotechnical behaviors
(Mohilever 2012). With the increase of such behaviors, there
is a need to systematically study such phenomena to be able
to assess the threats they could pose to human security. It
is important to know the motivations behind such acts, to
know the way they are formed, and to be able to predict
their outcome. Such studies could guide the development
of effective proactive measures and policies to eliminate the
security risks in cyberspace as well as physical spaces.
Parkour is the manifestation when traceurs (parkour practitioners) jump between distance rooftops, climb vertical
c 2015, Association for the Advancement of Artificial
Copyright Intelligence (www.aaai.org). All rights reserved.
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(a)
(b)
(c)
(d)
Figure 1: Examples of different forms of public performance. (a) Palestinians Practicing Parkour in Gaza, (b) a Flash Mob
Dance in a Shopping Mall, (c) Saudi Arabian Women’s Right to Drive Campaign’s Bumper Sticker, and (d) The 2011 Arab
Spring Social Movement.
cific gender, age, political affiliation, interest, and cultural
background) easier than it was before.
In this ongoing research, we focus on the flash mob behavior that takes place in the cyberspace but could possibly extend to the physical space. Here, we explore the factors that lead to this phenomenon and develop a conceptual
model that is able to predict the occurrence and the outcome
of such phenomenon. We look at both cyber as well as physical spaces, or “Cybernetic Space”, because as Mitra and
Schwartz claimed that in order “to understand how the notion of space is transformed by new technologies we need
to move away from the rigid definitions of real and cyber
spaces, to a flexible and relative conception of cybernetic
space” (Mitra and others 2001). Cybernetic space helps analyze the system and the interaction between its different
elements as a “whole” (Mohilever 2012).
As stated earlier, flash mobs pose non-negligible concerns
for public safety and national security, which warrants a systematic study to advance our understanding of such emerging sociotechnical behavior. Therefore, in this study, we
seek answers to the following questions: What are the factors that motivate an individual to participate in a flash mob?
What are the choices an individual could possibly have regarding acting (or not) in a flash mob?, and Using the factors
identified in the previous questions, can we develop a conceptual framework that is capable of modeling the formation
and predicting the outcome of a flash mob, i.e., success or
failure? Toward this direction, the article makes the following contributions, identify the factors that motivate an individual to act in a flash mob, develop a conceptual framework
that can predict the success or failure of a flash mob, and logically analyze the choices an individual has in case of acting
(or not) in a flash mob.
This research is an attempt to shed light on the emerging
sociotechnical behaviors, the risks they pose to human security, and advance our understanding of such behaviors by
bridging social and computational sciences.
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these laws should be evaluated under the Brandenburg test
(Steinblatt 2011).
Another research in this area focuses on the development
of flash mobs’ manifestations through the history. It also
shows the linkage of these manifestations with the new technologies and networking platforms and how these technologies influenced their formation, ideology, and diffusion (Mohilever 2012).
Flash mobs exhibit characteristics of complex social and
group dynamics and could be considered as manifestations
of collective actions. Various sociological theories such as
collective action (Coleman 1973), collective identity formation (Klandermans and others 2002) (Melucci 1996), and
collective decision (Coleman 1966) have been studied to explain complex social processes, such as social movements.
However, these theories have never been used to study or
model flash mobs, to the best of our knowledge. Understanding and modeling flash mobs is an important research
problem, which studies the factors that lead to this phenomenon and the factors that lead to its success or failure.
In the book by James S. Coleman entitled, “The mathematics of Collective Action” (Coleman 1973), Coleman
proposed a framework for collective action and provided
25 mathematical definitions within that framework. The
concepts of power, control, utility, and interest, defined by
Coleman are explored in our proposed model, in addition
to other concepts like social capital (Coleman 1966), hypergraph (Estrada and others 2006), and network modularity (Newman 2006). The concepts have been appropriately
modified for the cybernetic space.
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Methodology
In this paper, we focus on advancing our understanding of
the flash mob phenomenon by logically investigating the
factors that lead to its formation and to predict its success/failure. It is an attempt to explain the motivation needed
to sustain such highly coordinated acts. A flash mob is a
form of public participation that is primarily organized using cyber platforms. The action benefits multiple members
of the group (e.g., by accomplishment of the shared goal)
but has an associated cost (or risk) which is impossible for a
single member to undertake. Hence, the action is undertaken
collectively to share the cost (or, risk). Since flash mobs are
coordinated and could even be conducted online, there is a
heightened sense of motivation among members to connect
Literature Review
One of the research studies conducted in this field focuses on
the laws and regulations that can be posed against the people
who organize and participate in flash mobs that can lead to
violence, security threats, and crimes. Hannah Steinblatt argues the need to create “content-based” or “content-neutral”
laws to regulate digital speech that incites flash mobs and
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Figure 2: The factors that lead to the formation of flash mobs
and act online due to the anonymity and reduced personal
risk perception afforded by the cyber environment.
In analyzing flash mobs, social networks can be presented
as networks of individuals. Social networks thus contribute
significantly to individual participation. Here, prior social
ties operate as a basis for action, recruitment and established
social settings are the locus of flash mobs’ emergence. Typically, participants of a flash mob are linked through both
‘private’ and ‘public’ ties before a flash mob develops. Personal friends, relatives, colleagues, and neighbors may all
affect individual decisions to become involved in the flash
mob. Individuals may also be linked through indirect ties
generated by their joint involvement in specific activities
and/or events (e.g., following a meme or hashtag on social
media, affiliated to same organization, geographically colocated, ‘familiar strangers’, etc.).
3.1
Figure 3: Four possible scenarios flash mob practitioner
could face
ceeds certain (predetermined) threshold value, then we can
determine that a flash mob will more likely succeed. Otherwise, the flash mob is more likely to fail (see Figure 2).
The threshold value can be determined from historical data
on successful and failed flash mobs.
Logical Framework for the Formation of
Flash Mobs
3.2
Scenarios a Flash Mob Practitioner will Face
There are four possible scenarios that flash mobs practitioners could face. These scenarios will determine the decision
they make with regard to acting or not in a flash mob. Their
decision will be based on their interest in the flash mob and
the amount of control they have over it as shown in the four
scenarios below (also depicted in Figure 3):
1. If an individual has interest and has control then the likelihood of his/her participation is the highest, i.e., the individual will act.
2. If an individual has interest but does not have control
then he/she may act, or the likelihood of his/her participation is relatively lower than the previous case.
3. If an individual does not have interest, but has control
then the individual has two choices – either will withdraw,
i.e., will not act, or execute power exchange (i.e., relinquish power to possibly gain control over other events
(flash mobs) or to simply gain social capital). Social capital, as stated by Pierre Bourdieu, is “the value that one
gains from personal connections such as membership in a
family, an ethnic association, elite clubs, or other solidarity groups” (Biggart 2008).
4. If an individual has no interest and no control then the
individual will have two choices – either will simply withdraw or withdraw and act against the group.
The proposed model helps in categorizing individuals (or
nodes in the flash mob network) in one of the four aforementioned scenarios. Identifying the individuals in each of the
To model the phenomenon of flash mob, a deep study of
the sociological theories of collective action and collective
identity has been performed to extract the factors that lead
to these phenomena and their success or failure in the cybernetic space. Collective action can be defined as all activity
of common or shared interest among two or more individuals (Olson 1977). From the theories of collective action and
collective identity, we found out that flash mobs will occur
when the flash mob practitioners (nodes in the network) gain
some utility as Coleman argued in his book that “there is a
single action principal which governs the actions of the actors in the system: Each actor chooses those actions which
maximize his utility given environmental context created by
the events...” (Coleman 1973). Thus the utility difference
which is defined as the amount of utility gained by an actor (node) from the pair of possible outcomes (success or
failure) of the same event (flash mob) will determine their
interest in that flash mob. The more utility they will gain by
participating the more they are interested to participate and
vice versa.
Another factor that could affect flash mob participation
is control, i.e., to what extent an individual could affect the
outcome of the event (Coleman 1973). A flash mob practitioner needs control as well as interest in order for him/her
to be powerful. The more control and interest a flash mob
practitioner has the more powerful that individual (node) is.
By summing the power of all nodes we can find out the importance of a flash mob. If the importance of a flash mob ex-
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four possible scenarios help simplify the analysis of the flash
mob. It is quite evident that the individuals in the fourth scenario will not participate, meaning such individuals could be
eliminated from the analysis thereby removing noise from
the analysis. On the other hand, individuals from the first
scenario are the most likely to act, hence the analysis could
focus on such individuals.
The second and third scenarios are of equal importance
because the individuals in the third scenario provide the resources needed by the individuals in the second scenario,
i.e., control. The individuals of the second scenario are willing to gain control over the events that interest them to be
more powerful while the individuals of the third scenario
are willing to relinquish control over the events that do not
interest them. These two scenarios help demonstrate a possible condition of power exchange among individuals. If the
power exchange is executed, individuals belonging to second scenario would possibly act. Furthermore, monitoring
the nodes from the third scenario could help identify other
possible flash mobs.
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numbers: N000141010091 and N000141410489).
References
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Conclusions and Future Work
In this work, we shed light on a phenomenon that poses serious threats to human security in cyber as well as physical
spaces, or cybernetic space. The research explored the factors that can cause this increasingly occurring phenomenon
from the theories of collective action and collective identity
and designed a conceptual framework that is able to predict
the success or failure of the flash mob. We systematically
explored the options that a flash mob practitioner can have
when it comes to act or not (i.e., withdraw) in a flash mob
through four scenarios. We envision that this exploration
will help in bridging social science and computational science in order to form a comprehensive understanding of the
emerging sociotechnical behaviors, e.g., flash mobs.
As one of our immediate future directions, we plan to empirically evaluate the model using data collected from social
media sites. Unusually high number of flash mob occurrences have been reported during the holiday season. Moreover, unsurprisingly, most of them are organized using social
media platforms such as Twitter, Facebook, etc. As BART
spokesman Linton Johnson said “The difference between 10
years ago and now is massive... technology has just made it
easier to organize faster” (Steinblatt 2011). We will collect
data from social media sites using APIs targeting real-world
flash mobs, such as flashmobs.com which will be used to
collect data on various successful/failed FMs. We seek to
develop a socio-computational approach leveraging hypergraph constructs that will help in understanding the multidimensional and supradyadic dynamics of various social
processes, such as group formation, activation, decentralized
decision making, and collective action underlying flash mob
behaviors.
Acknowledgements
This research is funded in part by the U.S. National Science Foundation (award numbers: IIS-1110868 and IIS1110649) and the U.S. Office of Naval Research (award
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