Efficient estimation of influence functions for SIS model on social networks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Proceedings of the 20th international conference on World wide web
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Rumor has it: identifying misinformation in microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Rumors in a Network: Who's the Culprit?
IEEE Transactions on Information Theory
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.