Collective Classification for Social Media Credibility Estimation

dc.contributor.author O'Brien, Kyle
dc.contributor.author Simek, Olga
dc.contributor.author Waugh, Frederick
dc.date.accessioned 2019-01-03T00:02:03Z
dc.date.available 2019-01-03T00:02:03Z
dc.date.issued 2019-01-08
dc.description.abstract We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2019.271
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59662
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject Digital and Social Media
dc.subject collective classification, credibility, heterogeneous graphs, social media
dc.title Collective Classification for Social Media Credibility Estimation
dc.type Conference Paper
dc.type.dcmi Text
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