Epidemiological modeling of news and rumors on Twitter

Fang Jin, Edward Dougherty, Parang Saraf, Peng Mi, Yang Cao, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

134 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013
PublisherAssociation for Computing Machinery
ISBN (Print)9781450323307
DOIs
StatePublished - 2013
Event7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013 - Chicago, IL, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

NameProceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013

Conference

Conference7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013
CountryUnited States
CityChicago, IL
Period08/11/1308/14/13

Keywords

  • Epidemiological modeling
  • Rumor detection
  • SEIZ
  • SIS

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  • Cite this

    Jin, F., Dougherty, E., Saraf, P., Mi, P., Cao, Y., & Ramakrishnan, N. (2013). Epidemiological modeling of news and rumors on Twitter. In Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013 [8] (Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013). Association for Computing Machinery. https://doi.org/10.1145/2501025.2501027