Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2015

Abstract

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015)

Volume

3

First Page

1751

Last Page

1754

ISBN

9781450337946

Identifier

10.1145/2806416.2806607

Publisher

ACM Press

City or Country

Melbourne, Australia

Additional URL

https://doi.org/10.1145/2806416.2806607

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