SIDE Theory, Small World Networks, and Smart Mob Formation: A Beginners Guide

Submitted by: Scott Sanders to the Online Interaction Seminar, COM 632Y, Spring 2006, Dr. Sorin A. Matei, Purdue University, West Lafayette, IN, 47907.

Summary: Social categorization, which is at the heart of SIDE theory, may often prove to be an incredibly powerful dynamic in determining the actions of individuals in a smart mob. However, information provided via SMS messages activates social categorizations only when contextual cues make that information salient to the individuals. For example, an individual who is a fan of a certain movie star may receive a text message urging them to converge on a location at which the star has been seen. If they are close by and the detour costs them little time and effort, the individual may be more likely to follow the message’s suggestion than if they are across town and would be required to expend considerable time and effort to do so.

Article follows….

Over the past two decades the introduction of new communication technologies has changed how individuals interact with one another. The popularization of the internet encouraged the creation of online wiki communities in which all users have the ability to create, reorder, and edit content in such a way that no one person can be identified as the author of the final, and often continually evolving, piece. Likewise, blogging has taken publishing out of the hands of a few select gatekeepers and has allowed any individual with access to the internet to publish their own thoughts and opinions. Mobile phones have allowed individuals to reorder their lives and coordinate actions in a previously impossible manner. This availability of many-to-many forms of communication creates a necessary condition for the production of smart mobs, mobile ad hoc social networks, which are the result of individuals using personal communication technologies to coordinate collective action. Smart mobs seem to share at least some characteristics with the emergent behavior of swarm systems (Rheingold, 2002). However, there is one important difference between characteristic swarming behavior and that of smart mobs; individual human beings have considerable intellect. While there are several documented examples of smart mobs displaying emergent behavior, it is most important to consider the cognitive mechanisms and the network structures that create the prerequisite conditions for smart mob formation.

Smart mobs have been documented in a number of different contexts. An exemplar of smart mob behavior is the 2001 toppling of the Estrada administration in the Philippines when citizens were urged to take to the streets wearing black (Rheingold, 2002; Bociurkiw, 2001). A more recent example is the French race riots of the summer of 2005 during which police revealed that rioting teenagers were using the internet and short message service (SMS) messages to coordinate attacks (Smith, 2005). Likewise, during December of 2005 Australia had to contend with youth using SMS messages to instigate and coordinate violence aimed at individuals of Middle Eastern descent in response to the beating of two lifeguards (BBC, 2005).

Although incompatible networks initially hampered their adoption in the United States, many countries have long had a booming SMS trend because of its relative cheapness compared to phone calls. SMS messages have proven an important mechanism in the development of smart mobs for several reasons. First, SMS is an asynchronous communication which allows its users to exert more cognitive effort on expressing and editing thoughts. Second, technological constraints force messages to be brief and to the point allowing them to be created and sent quickly. SMS technology allows brief written messages of a maximum of 160 characters to be sent and received via mobile phones (Featherly, 2003). Furthermore, messages can be sent or forwarded to everyone in an individual’s address book. This is allows a message to be disseminated more quickly than a traditional phone tree where each individual would have to be contacted separately, thereby allowing large networks to be mobilized relatively quickly (Walker, 2003). Finally, SMS messages are sent to mobile phones rather than email inboxes or hard lines. One of the mobile phone’s primary functions is to serve as a coordination tool. Mobile phones allow micro-coordination of behavior by allowing for midcourse adjustments and the “softening” of time where the timing of schedules and events are negotiated or reordered (Ling, 2004; Ling & Yttri, 2002).

One explanation for the creation of smart mobs is the threshold models of collective behavior. Threshold models propose that individuals have a particular threshold at which they are willing to engage in collective action (Granovetter, 1978). Threshold models attempt to explain why individuals may be willing to participate in actions collectively that they would not be willing to participate in alone. The argument hinges upon individuals conducting a cost-benefit analysis which weighs the rewards of engaging in the behavior against the possible repercussions. The more people that choose to participate in particular actions the less likely an individual will be held accountable for their behavior. Some individuals require very few individuals to participate prior to joining in, while others may wait for a majority of the population to engage in a behavior before to taking action. Furthermore, since the threshold is simply the point in which an individual chooses to engage in a behavior “two individuals whose thresholds are the same may not be politically identical, as reflected in the popular expression “strange bedfellows”(Granovetter, 1978, pg. 1422). In short, individuals do not have to share the same motivation to engage in a behavior, they merely must have their threshold level met.

Although the threshold theory holds that individuals need not share an ideology for collective action, Ling (2004) notes that “these social aggregates [smart mobs] function as a unit so long as there is a shared ideology and a common sense of strategy, and so long as there is a focused easily communicated form of interaction” (pg. 187). Therefore, rather than individuals merely having a threshold that must be exceeded in order to take action, individuals in smart mobs may require a shared sense of identity. Social identity deindividuation theory (SIDE) was developed to explain online group interaction through people’s identification with social identities. Its basic tenet is that text based environments, such as the internet, served to limit nonverbal cues so that individuals are deindividuated (Walther & Parks, 2002). Deindividuation is the loss of self-awareness and critical evaluation of actions as a result of the anonymity created by group scenarios. For example, larger group sizes have been found to facilitate behavior that contradicts societal norms such as taunting suicides to jump or joining a lynch mob (Mann, 1981; Mullen, 1986). Furthermore, anonymity under conditions of deindividuation has been found to result in salience given to contextual cues concerning how to behave (Johnson & Downing, 1979; Spivey & Prentice-Dunn, 1990). Considered in the context of communication media, it has been found that different media may result in the personal and social attributes being more or less salient. For example, when individuals were individuated by being placed in the same room as one another during interaction they were less likely to comply with group norms and more likely to exert an independent identity (Postmes, Spears, and Lea, 1998). Deindividuation in turn affects whether interpersonal or intergroup differences matter during interaction (Postmes & Baym, 2005).

When people cannot individuate others they are forced to rely on contextual cues that indicate the social identities of group members. Social identities do not just consist of an individual’s understanding of a group or social category, but are a shared conception within a group of the defining features of group membership (Postmes & Baym, 2005). Along with norms, social identity can mold group action as a result of social identification and social categorization. Social identification is the internalization of a social identity resulting from long term identification with a particular group and so that group norms are subsequently adopted as personal norms. Conversely, categorization is the result of social context increasing the salience of particular social categories. In short, rather than relating to others as individuals, SIDE theory proposes that, in conditions of limited information and subsequent deindividuation, people relate to one another on the basis of group membership.

Initially, it seems unlikely that SIDE theory can provide an explanation of group behavior in smart mobs. First, individuals who receive a message calling for a smart mob to coalesce may know the sender. After all, the primary way that smart mob messages spread is via forwarded messages sent to multiple receivers in the sender’s address book. The key rests in the receiver’s media literacy. It is common for received text messages to be forwarded to others in some cultures, for example, teen cultures of Asian and
Scandinavian countries. If individuals perceive the message as originating from the immediate sender, then SIDE effects probably will not be observed because too much individuating information is known about the sender. That is, the receiver is more likely to relate to the sender on an interpersonal level rather than a group level. However, if the receiver interprets the message as a call for action as having not originated from the immediate sender then it is possible that they will identify with the message on a group level and respond to the cues embedded in the message.

SMS technology may be highly conducive to producing anonymous messages that can be used to galvanize support for causes and make calls for action. For example, during the SARS crisis of spring 2003, SMS messages circulated in China satirically poking fun at government officials and protocols and urging individuals to stock up on rice and salt (Yu, 2004). The events in China highlight the subversive potential of SMS as a tool for protest. The anonymity afforded the initial composer of a message and those who forwarded it can be used to oppose powerful social institutions from a safe distance. Furthermore, SMS’s text based nature, along with its limitations on the number of characters a message can contain make it an extremely lean medium which may contribute to the activation of a social identity rather than an individual identity.

The second factor that interferes with the application of SIDE theory to smart mobs is that many smart mobs do not have a great deal of interaction. Social identity is formed not only from “common perspectives of group history and a sense of future direction but most importantly through comparison and differentiation from relevant outgroups” (Postmes & Baym, 2005, pg. 224). This process of developing a group identity through the creation of a shared history and comparison to outgroups is accomplished by individuals using the content of others messages to create and strengthen group norms (Walther & Parks, 2002). However, this presents a quandary in the context of smart mobs that may have little interaction prior to the initial call to action. Even if a receiver contacted the immediate sender of a forwarded message, the sender would be unlikely to be able to inform them or assist them in identifying others who might have received the same message outside of their own personal contacts.

Rather than developing a social identity through mediated interaction, it is possible that individuals who respond to the initiating message of a smart mob already possess a salient social identity that was formed during everyday interaction in society in which they live. It should be noted that the best documented occurrences of smart mob behavior are in contexts such as political dissent, race riots, and celebrity stalking. Individuals who are involved in these activities may already have preformed identities that are activated by the reception of a SMS message.

Postmes and Baym (2005) have noted that individuals’ social identity can be made salient by features of the social context that do not require the presence of other group members. Therefore, social categorization may often prove to be an incredibly powerful dynamic in determining the actions of individuals in a smart mob. However, information provided via SMS messages activates social categorizations only when contextual cues make that information salient to the individuals. For example, an individual who is a fan of a certain movie star may receive a text message urging them to converge on a location at which the star has been seen. If they are close by and the detour costs them little time and effort, the individual may be more likely to follow the message’s suggestion than if they are across town and would be required to expend considerable time and effort to do so. The message coupled with proximity provides contextual cues that activate the social identity of a fan. Likewise, events may also provoke social categorization. For example, the attack on two lifeguards at Cronulla beach in Sydney, Australia by youths perceived to be of Lebanese descent may have increased the salience of ethnic identity to the point that white youth could be mobilized via SMS messages.

Not only is it important to consider the cognitive mechanisms that lead individuals to participate in smart mobs, it is also important to consider the network structure that allows smart mobs to form. Specifically, small world networks may be an essential component of the development of smart mobs. Small world networks were first proposed in the 1950’s in an effort to estimate the number of links required to connect any two individuals living within the United States (Monge & Contractor, 2003). By considering the full range of an individual’s social contacts they concluded that the majority of individuals would be linked by two to three others. This conclusion was tested in 1967 by Stanley Milgram who asked individuals in Nebraska and Kansas to send a letter to Boston via intermediaries. Half of the letters were received after going through no more than 5 individuals.

Several types of networks have the potential to form in the real world. Small world networks are characterized by a high degree of clustering among local nodes with a few far flung links to distant nodes so that all nodes are separated by no more than a few links (Monge & Contractor, 2003). Regular networks consist of nodes with a few links to only their immediate neighbors. While regular networks demonstrate high degrees of clustering, nodes may be separated by numerous links. Finally, random networks exist where the nodes are randomly linked. Random networks lack clustering but do have a low degree of separation between nodes. Watts (1999; as cited in Monge & Contractor, 2003) believes that small world networks, rather than regular or random networks, comprise a majority of networks found in the real world.

Small world networks have important implications for the formation and function of smart mobs. Small world networks help explain how smart mobs form quickly. Milgram’s experiment shows that human social networks are, or at least approximate, small world networks. Smart mobs are initiated via text messages sent to multiple individuals in the sender’s address book and, therefore, can be considered a subset of an individual’s social network. Still, a majority of people whom the immediate sender of a smart mob SMS knows are likely not included in his address book. This limited list of contacts still may be sufficient to result in a small world network. Although lower link density between individuals in the network might inhibit the spread of an SMS, the nature of SMS technology, which forwards of the messages to large groups of people quickly, could counteract this by allowing for the messages’ quick and efficient transmission. There are currently approximately 2 billion mobile phones on the planet and many individuals who do not have access to other forms of new media, such as the internet, that allow many-to-many communication own mobile phones (Gunn, lecture). Such was the case in the Philippines during the 2001 protests against President Estrada. Much of the population lives in poverty and SMS provides a cheap, convenient method of communication (Bociurkiw, 2001). Although mobile phones were far from ubiquitous, small world social networks were likely a decisive factor in allowing messages calling for protest to quickly spread.

We must also take into consideration other mechanisms that effect network structure and the transmission of information. Proximity of nodes to one another may be especially important. When considering personal networks nodes are not chosen randomly but are “inversely proportional to the square of its geographical distance from the originating node” (Monge & Contractor, 2003, pg. 312). In other words, the closer two nodes are to one another the more likely they are to form a connection. Proximity is not only important for creating new connections but it also plays an important role in the maintenance and dissolution of established ones. Another factor that likely plays a role in transmission of messages to initiate a smart mob is homophily, or the extent to which individual’s are similar. When homophily and proximity are jointly taken into account, individuals are much more effective in reaching their intended target with a message.

Both proximity and homophily may provide partial explanations of how smart mobs form. If mobile phone address books are merely subsets of our social networks, then small world network structures that take into account proximity suggests that a bulk of message recipients will be geographically proximal. This is important because smart mobs gather relatively quickly. Proximal individuals would be able to reasonably travel to the appointed destination without undue effort. Furthermore, this structure also suggests that individuals who would be constrained from participating in smart mobs by geographical distance would be less likely to receive the initiating message. Of course, proximities constraints on smart mob participation likely play little role in online smart mobs. Homophily may also play an important role in the spread of the initial smart mob message. If individuals are more likely to form social linkages with others that they perceive to be similar to themselves then individuals whose social identity is activated by a message are more likely to pass along that message to others than those who do not share that social identity. Not only would homophily facilitate the dissemination of messages but it might also act as a filtering mechanism to prevent people to whom the message would be irrelevant from receiving it. People who do not identify with the message may be less likely to pass the message on to others. As a result, the message is spread among those for whom it activates a social identity, and disregarded by those for whom it does not.

Ling (2004) notes that smart mobs are anomalies in the larger picture of mobile communication and arise only under specific conditions. First, he noted the social contextual features that promote a common ideology are necessary to develop the desire to take action. He illustrates this by showing how dissatisfaction with the corrupt Estrada regime in the Philippines predisposed individuals to protest. Second, he focused on the necessity of a clear, concise strategy for spurring action. Philippine citizens were encouraged to go to a well known, symbolic location to engage in protest. Finally, he describes that the necessity of SMS as an easy and efficient channel for the spread of the initiating messages. Messages were forwarded via the protestors address books to others who might respond.

SIDE theory and small world networks fit neatly into this framework of the necessary preconditions for initiating a smart mob. First, individuals must have the necessary social identity for a smart mob to form. People will not respond to any message, just the ones that they feel are relevant to them. Second, while the social identities that could potentially lead to a smart mob likely persist over time, the social climate must be exact in order for smart mobs to develop. Social identities must be activated via the process of social-categorization by contextual features in the environment or by interaction with others. Additionally, others do not have to be present for this to occur. Contextual cues, such as the beating of the lifeguards at Cronulla beach, increase the salience of social identities so that they can potentially be activated by an SMS calling for specific action. It is for this reason that smart mobs are relatively uncommon. Finally, the nature of social networks as small world networks coupled with SMS technology is essential to the development of smart mobs. SMS provides a method for alerting many people simultaneously to the call for the formation of a smart mob. Small world networks’ structures facilitate the spread by allowing messages to be received by individual nodes with a minimal number of linkages. The combination of these factors allow smart mobs to form instantaneously. Further network mechanisms serve to filter and promote the spread of initiating messages.

Smart mobs have been used purposefully to accomplish tasks impossible for a single hierarchical organization. Many of the same conditions that allow smart mobs to form also create conditions in which they can display emergent behavior. First, the nature of many-to-many communication mediums means that leadership is decentralized. This means that disabling one node in the network will not cripple it. A notable example of this is the Direct Action Network which used mobile communication devices to coordinate protest of the World Trade Organization in Seattle. Arresting “ring leaders” did not slow the attacks or seriously hamper coordination of efforts (de Armond, 2000). Second, human beings are autonomous and make the choice to submerge their personal identity in favor of a social one. Furthermore, as individuals they have street level data and do not have an overall picture of the scenario. The conciseness of text messages mean that complex strategies cannot be laid out in detail but must evolve as events unfold. Finally, the high connectivity provided by SMS technology allows individuals to coordinate action by converging on a target from many directions and then dispersing just as quickly. This phenomenon has been labeled “swarming” in the contexts of political protests (Ronfeldt & Arquilla, 2001) but can also be observed in contexts as diverse as celebrity watching or article editing on wikipedia. As a result of the role peer influence plays in a smart mob, the network structure of a smart mob, and the high connectivity that characterizes it, smart mobs can be highly adaptive and unpredictable.

The application of SIDE theory to mobile ad hoc mobile networks helps illustrate how difficult, if not impossible, it would be to intentionally start a smart mob if the prerequisite conditions were not in place. When the conditions are right it may be possible to initiate and determine the initial trajectory of a smart mobs actions, but then it takes a life of its own. However, starting a smart mob from scratch may be impossible. A man pleads in comment left on the Smart Mob blog, “Help Im running for polictical [sic] office, and i [sic] want to use this method to reach the voters who are on the net. how [sic] do I go about doing this.[sic]” (Rheingold & Grayman, 2003). Appropriately, he never received a response.

Works Cited

BBC News (2005). ” Howard condemns riots in Sydney.” Retrieved May 2, 2006, from

Bociurkiw, M. (2001). “Revolution by Cell Phone.” Retrieved May 2, 2006, from

de Armond, P. (2000). “Black Flag Over Seattle.” Retrieved May 2, 2006, from

Featherly, K. (2003). Short Messaging System. Encyclopedia of New Media. S. Jones.
Thousand Oaks, CA, Sage: 408-411.

Granovetter, M. (1978).Threshold models of collective behavior. American
Journal of Sociology. 83(6), 1420-1443.

Johnson, R.W., & Downing, L.J. (1979). Deindividuation and valence of cues: Effects of
prosocial and antisocial behavior. Journal of Personality and Social Psychology, 37, 1532-1538.

Ling, R. (2004). The Mobile Connection: The Cell Phone’s Impact on Society. San
Francisco, Elsevier.

Ling, R. and B. Yttri (2002). Hyper-coordination Via mobile phones in Norway.
Perpetual Contact: Mobile Communication, Private Talk, Public Performance. J. E. Katz and M. Aakhus. Cambridge, Cambridge University Press: 139-169.

Mann, L. (1981). The baiting crowd in episodes of threatened suicide. Journal of
Personality and Social Psychology, 55, 703-709.

Monge, P. R., & Contractor, N. (2003). Multitheoretical, Multilevel Models of
Communication and Other Organizational Networks . In Theories of Communication Networks (pp. 293-327). New York: Oxford University Press.

Mullen, B. (1986). Atrocity as a function of lynch mob composition: A self-attention
perspective. Personality and Social Psychology Bulletin, 12, 187-197.

Postmes, T., & Baym, N. (2005). Intergroup dimensions of the Internet. In J. Harwood &
H. Giles (Eds.), Intergroup communication: Multiple perspectives (pp. 213-238). New York: Peter Lang.

Postmes, T., Spears, R., & Lea M. (1998). Breaching or building social boundaries?
side-effects of computer-mediated communication. Communication Research, 25(6), 689-715.

Rheingold , H. (2002). Smart mobs: The next social revolution. Cambridge, MA:

Rheingold, H. and Grayman. (2003). “Smartmobs Sway Korean Elections?” Retrieved
May 2, 2006, from

Ronfeldt, D., and Arquilla, J. (2001). “Networks, netwars, and the fight for the Future.” Retrieved May 2, 2006, from

Spivey, C.B., & Prentice-Dunn, S. (1990). Assessing the directionality of deindividuated
behavior: Effects of deindividuation, modeling, and private self-consciousness on aggressive and prosocial responses. Basic and Applied Social Psychology, 11, 387-403.

Walker, J. (2003). Is That a Computer in Your Pants? Reason. 34: 36-42.

Walther, J. B., & Parks, M. R. (2002). Cues filtered out, cues filtered in: Computer-
mediated communication and relationships. In M. L. Knapp & J. A. Daly (Eds.), Handbook of interpersonal communication (3rd ed., pp. 529-563). Thousand Oaks, CA: Sage.

Watts, D.J. (1999). Small worlds: The dynamics of networks between order and
randomness. Princeton, NJ: Princeton University Press.

Yu, H. (2004). The power of thumbs: The politics of sms in urban China. Graduate
Journal of Asia-Pacific Studies, 2 (2), 30-43.

4 thoughts on “SIDE Theory, Small World Networks, and Smart Mob Formation: A Beginners Guide

Leave a Reply

Your email address will not be published. Required fields are marked *