Edward Dougherty, Parang Saraf, Yang Cao, Naren Ramakrishnan

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.

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Naren Ramakrishnan


Parang Saraf


Publication Details

Date of publication:
August 8, 2013
Conference:
SIGKDD international conference on Knowledge discovery and data mining
Publisher:
Association for Computing Machinery (ACM)