What Stops Social Epidemics?

By Greg Ver Steeg, Rumi Ghosh, Kristina Lerman. 2011

98 citations recorded by the source corpus.

Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics, Social Sciences & Humanities, Network Science & Complex Systems

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Citations in the corpus

Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, background): “Similarly, the more friends share some information, the less likely the user is to share it with his or her followers [10].”
Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “Regardless of what measure is used as a proxy of quality—comment length, the number of responses or upvotes from others it receives, its textual complexity—the quality of each successive comment written by a Reddit user decreases over the course of a session [22].”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “We propose that decay of visibility combined with divided attention explains why most information cascades in social media fail to reach epidemic proportions [26].”
Inferring Networks of Diffusion and Influence (2012, background): “… et al. 2004; Kumar et al. 2004; Leskovec et al. 2006a, 2006b, 2007c; Liben-Nowell and Kleinberg 2008], there have been only a few studies of the actual shapes of cascades [Ghosh and Lerman 2011; Leskovec et al. 2007c; Liben-Nowell and Kleinberg 2008; Romero et al. 2011; Ver Steeg et al. 2011].”
Binary-state dynamics on complex networks: pair approximation and beyond (2012, unknown): “Recently, data from online social networks has been mined to help increase the understanding of how individuals are influenced by their network neighbors [16, 17, 18, 19, 20, 21]; further insight is provided by experiments where the network topology and social interactions are carefully controlled [”
The Simple Rules of Social Contagion (2013, background): “Our work clears the confusion and shows that there are important and surprising diﬀerences between the spread of information and the spread of disease, stemming from human cognitive limitations for discovering information.”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, methodology): “Figure 1 shows the size of outbreaks simulated using the ICM on a social media follower graph (red dots) [14].”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, background): “Our simulated results provides an explanation to why most information cascades in social media fail to reach epidemic proportions (Ver Steeg, Ghosh, and Lerman 2011).”