Humans often gather into large groups–crowds. As part of crowds, do we act and think differently than we would if we were isolated or in a smaller group? Do large crowds represent dangerous and unstable situations? Is there actually such a thing as “mob rule”? Or, do crowds actually bring about social change?
What about virtual crowds? Social media creates large virtual gatherings of individuals. Do virtual crowds act like physical crowds? Could they be dangerous and unstable?
What does the research say?
Bibliography: Recent and classic publications about the behavior, social change, movement, collective action, social media, context, and identity of crowds.
*Drury, J., & Reicher, S. (2000). Collective action and psychological change: The emergence of new social identities.The British Journal of Social Psychology, 39, 579-604. [PDF] [Cited by]
“For over a century, psychological analyses of crowds have stressed their irrationality and their destructiveness. In recent years, there have been a number of studies which argue by contrast that crowd action is socially meaningful. This study addresses how crowd action does not only reflect social meanings, but can also create and develop new social meanings. In other words, we want to show that crowd events are marked by the simultaneous co-occurrence of social determination and social change and therefore encapsulate what is one of the key paradoxes of the social sciences. For Le Bon, then, crowds are inherently conservative, showing a fetish-like respect for ‘traditions’ and an `unconscious horror of all novelty.” However, empirical studies tell a very different story … argue that ‘people power’ helps to explain such events as the ‘velvet revolutions’ in Europe in 1989, the fall of Marcos in the Philippines in 1986, aspects of the Palestinian Intifada and South African anti-apartheid struggle and many other key political events. Historical research on popular actions of the 17th, 18th and early 19th centuries also contradicts Le Bon’s picture of the ineffective, conservative crowd. Moreover, examination of the actions of participants in such crowd events suggests that patterned changes occurred in the identities and social representations of participants. For example, analysts of the waves of collective action in the USA in the 1960s note the enduring ‘radicalization’ among activists. Similarly, participants in mass strikes have been seen to develop a more critical attitude towards those in power and a more class-collective self-conception.”
*Moussaïd, M., Helbing, D., & Theraulaz, G. (2011). How simple rules determine pedestrian behavior and crowd disasters.Proceedings of the National Academy of Sciences of the United States of America, 108(17), 6884-6888. [PDF] [Cited by]
“Human crowds display a rich variety of self-organized behaviors that support an efficient motion under everyday conditions. One of the best-known examples is the spontaneous formation of unidirectional lanes in bidirectional pedestrian flows. At high densities, however, smooth pedestrian flows can break down, giving rise to other collective patterns of motion such as stop-and-go waves and crowd turbulence. The latter may cause serious trampling accidents during mass events.
At high densities [of crowding], physical interactions start to dominate over the heuristic-based walking behavior. As the interaction forces in the crowd add up, intentional movements of pedestrians are replaced by unintentional ones. Hence, the well-coordinated motion among pedestrians suddenly breaks down, particularly around bottlenecks. This breakdown results in largely fluctuating and uncontrollable patterns of motion (crowd turbulence). The combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities—a phenomenon that has been observed during recent crowd disasters.
By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms.”
*Neubaum, G., & Krämer, N. C. (2017). Monitoring the opinion of the crowd: Psychological mechanisms underlying public opinion perceptions on social media.Media Psychology, 20(3), 502-531. [Cited by]
“Social media technologies offer several features that allow users to monitor other people’s opinions on public issues. Initial research showed that user-generated content can shape recipients’ perceptions of the majority opinion on societal problems. Still, it remains largely unexplored under which circumstances people gauge other users’ opinions through social media and whether perceived opinion climates affect people’s opinions and communication behavior in these environments. Results of a two-session experiment revealed that people’s fear of isolation sharpens their attention toward user-generated comments on Facebook which, in turn, affect recipients’ public opinion perceptions. The latter influenced subjects’ opinions and their willingness to participate in social media discussions. User-generated comments stand out against the number of likes, as the former were attended to more thoroughly by users and also had larger effects on users’ public opinion perceptions. This research points to the potential of opinion cues on social media to weaken cognitive biases, as user-generated comments were shown to attenuate the human tendency to project one’s opinion onto others.”
*Reicher, S. D. (1996). “The battle of Westminster”: Developing the social identity model of crowd behaviour in order to explain the initiation and development of collective conflict.European Journal of Social Psychology, 26(1), 115-134. [PDF] [Cited by]
“… It reaffirms the relevance of social identity and self-categorization processes to collective action. This can be seen in a number of different ways: the initiation of conflict depended upon the meaning of outgroup action in terms of the collective beliefs of the student category; joint participation in the conflict depended upon adopting a common self-categorization as student in opposition to the police: the treatment of others depended upon their categorical relationship to the self – such that individuals would risk arrest in order to defend other students with whom, on a personal level, they were unacquainted; the response to conflictual acts depended upon their consonance with categorical beliefs – thus only actions seen as ‘defensive’ rather than ‘offensive’ generalized through the crowd. While this analysis was based on self-categorization theory, it has implications for how the theory needs to be developed. Most notably, this concerns the relationship between intra- and intergroup levels of analysis. Self-categorization theory acknowledges this relationship by showing how the character of social categories is produced by the intergroup context.
What emerges from Westminster Bridge is that context should not be seen as an external reality that determines human actions and perceptions. Rather context is itself produced out of action on the basis of categorization. Moreover, rather than categorization and context being opposed terms, it has been shown that the categorizations employed by a first group [students] may, as a function of intergroup power relations, form the concrete context in which a second [police] categorizes itself, perceives the first and acts in turn towards it. Hence categorization is constantly mutating into context and vice versa as a function of intergroup relations. This means that any thorough understanding of group salience, group stereotypes or group empowerment needs to be embedded in an historical study of these evolving relations between groups. Finally, this study suggests that crowd action is not only socially patterned, but also that it brings about social change.”
For additional research about crowds, please see the Science Primary Literature Database.
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