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The Social Domino: How Mark Granovetter Decoded Riots and Viral Trends

Psychologists assumed that violent mob riots were caused by crowds filled with violent individuals; Mark Granovetter proved that peaceful citizens will join a riot if each person’s individual threshold triggers the next person like falling dominoes. Published in 1978, Granovetter’s "Threshold Model" founded the mathematics of social contagion, explaining how viral internet memes, financial bank runs, and sudden political revolutions explode into reality.

Author
Mark Granovetter
Published
1978
Journal
American Journal of Sociology
Last updated
September 2026
The Social Domino: How Mark Granovetter Decoded Riots and Viral Trends

In crowd psychology, when a peaceful street protest suddenly turns into a riot or a new fashion trend goes viral overnight, observers assume the entire crowd suddenly went crazy or shared identical extreme desires.

Stanford sociologist Mark Granovetter proved that collective actions are like falling dominoes: everyone has a different personal "threshold"—the number of people who must act before they join in. An agitator needs zero followers (threshold 0); a radical needs one follower (threshold 1); an ordinary citizen needs twenty followers. If there is a clean chain (0, 1, 2, 3...), a single rock thrown by one person cascades into a 100-person riot.

If just one person in the chain is missing (e.g., no one with threshold 2), nothing happens. By explaining viral social media adoption, by modeling sudden panic bank runs on Wall Street, and by mapping viral crowd dynamics, threshold sociology decodes collective human behavior.

Reference

Granovetter, M. (1978). Threshold Models of Collective Behavior. American Journal of Sociology, 83(6), 1420–1443.

Title

Threshold Models of Collective Behavior

Abstract

Models of collective behavior are developed for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative. The key concept is that of "threshold": the number or proportion of others who must make one decision before a given actor does so; this is the point where net benefits begin to exceed net costs for that particular actor. Beginning with a frequency distribution of thresholds, the models allow calculation of the ultimate or "equilibrium" number making each decision. The stability of equilibrium results against various possible changes in threshold distributions is considered. Stress is placed on the importance of exact distributions distributions for outcomes. Groups with similar average preferences may generate very different results; hence it is hazardous to infer individual dispositions from aggregate outcomes or to assume that behavior was directed by ultimately agreed-upon norms. Suggested applications are to riot behavior, innovation and rumor diffusion, strikes, voting, and migration. Issues of measurement, falsification, and verification are discussed.

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