1 research outputs found
On the Coherence of Fake News Articles
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 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.Comment: 8th International Workshop on News Recommendation and Analytics (INRA
2020) held in conjunction with ECML PKDD 2020 Conferenc