The Simple Rules of Social Contagion

By Nathan Oken Hodas, Kristina Lerman. 2013

313 citations recorded by the source corpus.

It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention and explicit social feedback to predict the temporal dynamics of user behavior.

Subfields: Cognitive Neuroscience, Social Psychology, Sociology and Political Science

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Citations in the corpus

Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, background): “Once data is disaggregated based on the volume of information individuals receive, a clearer pattern of response emerges, one that is more predictive of behavior [13].”
Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “Once data are disaggre-gated based on the volume of information individuals receive [19], a clearer pattern of response emerges, one that is more predictive of behavior [10].”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “Hodas and Lerman (2014) use an explicit model of user attention and visibility to explain differences in information diffusion between Digg and Twitter.”
Topological Recurrent Neural Network for Diffusion Prediction (2017, background): “• Twitter [38] contains the diffusion of URLs on Twitter during 2010 and the follower graph of users.”
Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Further studies found that the relationship could vary across topics [45], across number of friends due to the information overload of highly connected users [46, 47], and across community structures [48].”
Evidence of Online Performance Deterioration in User Sessions on Reddit (2016, background): “Only recently, our research community started investigating the possible relationship between cognitive limits and online interactions, showing the impact of information over-load on user behavior [22, 20, 23, 24].”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “The eﬀect of social signals on user’s likelihood to become “infected” can be measured experimentally [35] or estimated from observational data [39].”
Dynamics of information diffusion and its applications on complex networks (2016, background): “For the case of large cascades, the number of the new approved individuals always follows power-law relaxation after the burst [7,8].”