Epidemiological modeling of news and rumors on Twitter

By Fang Jin, Edward R. Dougherty, Parang Saraf, Yang Cao, Naren Ramakrishnan. Social Network Mining and Analysis, 2013

373 citations recorded by the source corpus.

Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/37aaa2c2da2876ca5ebbfe396f942d3094d5ee30

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In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks (2018, background): “(Jin et al. 2013) utilize epidemiological models to characterize information cascades in Twitter resulting from both true news and fake news.”
False Information on Web and Social Media: A Survey (2018, background): “[41] created a false information propagation model which they called SEIZ.”
False rumors detection on Sina Weibo by propagation structures (2015, methodology): “Researchers also use time to calculate rates of population change among the compartments in epidemiological models [24], [32].”
The spread of true and false news online (2018, background): “Some work develops theoretical models of rumor diffusion [37, 38, 39, 40], or methods for rumor detection [41, 42, 43, 44], credibility evaluation [45] or interventions to curtail the spread of rumors [46, 47, 48].”
Combating Fake News (2019, background): “[44] quantify a ratio (RSI ) as the ratio of influx into E from S to the outflow from E to I and suggest that it can potentially be used to detect rumors, where they found that higher RSI is suggestive of true news based on the studied cascades.”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “How and why rumors spread on Twitter has been the subject of several studies [34, 26, 12].”
Spread of Hate Speech in Online Social Media (2018, background): “However, there are several works that looks into diffusion of fake news [26, 37, 41], Linkedin [3], retweet cascade [8, 9, 18, 37], rumours [11, 17, 20, 22, 45] and Tumblr [1, 2, 7, 42].”