%0 Conference Paper %1 Jin:2013:EMN:2501025.2501027 %A Jin, Fang %A Dougherty, Edward %A <b>Saraf, Parang</b> %A Cao, Yang %A Ramakrishnan, Naren %B Proceedings of the 7th Workshop on Social Network Mining and Analysis %C New York, NY, USA %D 2013 %I ACM %K SEIZ SIS detection epidemiological modeling myown rumor twitter %P 8:1--8:9 %R 10.1145/2501025.2501027 %T Epidemiological Modeling of News and Rumors on Twitter %U http://doi.acm.org/10.1145/2501025.2501027 %X Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underly- ing media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological mod- els 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 ap- proach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors. %@ 978-1-4503-2330-7

@inproceedings{Jin:2013:EMN:2501025.2501027, abstract = {Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underly- ing media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological mod- els 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 ap- proach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.}, acmid = {2501027}, added-at = {2014-10-23T22:43:10.000+0200}, address = {New York, NY, USA}, articleno = {8}, author = {Jin, Fang and Dougherty, Edward and <b>Saraf, Parang</b> and Cao, Yang and Ramakrishnan, Naren}, biburl = {https://www.bibsonomy.org/bibtex/2fa205ee20cd9e3b3ce5e518d9af87dc2/parangsaraf}, booktitle = {Proceedings of the 7th Workshop on Social Network Mining and Analysis}, doi = {10.1145/2501025.2501027}, interhash = {c3aeeda6d5ff9393bdff7ba85af0e024}, intrahash = {fa205ee20cd9e3b3ce5e518d9af87dc2}, isbn = {978-1-4503-2330-7}, keywords = {SEIZ SIS detection epidemiological modeling myown rumor twitter}, location = {Chicago, Illinois}, numpages = {9}, pages = {8:1--8:9}, publisher = {ACM}, series = {SNAKDD '13}, timestamp = {2014-10-23T23:20:13.000+0200}, title = {Epidemiological Modeling of News and Rumors on Twitter}, url = {http://doi.acm.org/10.1145/2501025.2501027}, year = 2013 }