@inproceedings{9c44b7b5199f438994722d4394440808,
title = "Epidemiological modeling of news and rumors on Twitter",
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.",
keywords = "Epidemiological modeling, Rumor detection, SEIZ, SIS",
author = "Fang Jin and Edward Dougherty and Parang Saraf and Peng Mi and Yang Cao and Naren Ramakrishnan",
year = "2013",
doi = "10.1145/2501025.2501027",
language = "English",
isbn = "9781450323307",
series = "Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNA-KDD 2013",
note = "null ; Conference date: 11-08-2013 Through 14-08-2013",
}