On the Coherence of Fake News Articles

Iknoor Singh, Deepak P., Anoop K

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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The generation and spread of fake news within new and online media sources is emerging as a phenomenon of high societal significance. Combating them using data-driven analytics has been attracting much recent scholarly interest. In this computational social science study, we analyze the textual coherence of fake news articles vis-a-vis legitimate ones. We develop three computational formulations of textual coherence drawing upon the state-of-the-art methods in natural language processing and data science. Two real-world datasets from widely different domains which have fake/legitimate article labellings are then analyzed with respect to textual coherence. We observe apparent differences in textual coherence across fake and legitimate news articles, with fake news articles consistently scoring lower on coherence as compared to legitimate news ones. While the relative coherence shortfall of fake news articles as compared to legitimate ones form the main observation from our study, we analyze several aspects of the differences and outline potential avenues of further inquiry
Original languageEnglish
Title of host publicationECML PKDD 2020 Workshops
ISBN (Electronic)978-3-030-65965-3
Publication statusPublished - 02 Feb 2021
Event8th International Workshop on News Recommendation and Analytics (INRA 2020) -
Duration: 14 Sep 202014 Sep 2020

Publication series

NameECML PKDD Workshops
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Workshop8th International Workshop on News Recommendation and Analytics (INRA 2020)
Internet address


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