On the coherence of fake news articles
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 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.
Fake News Detection
591-607
Singh, Iknoor
20f9c5b6-5d83-4583-afbe-118ac7cd61ba
P, Deepak
1c9443bb-3a4f-49c7-b42f-b978ae5b2a11
Kadan, Anoop
9cc17e26-a329-49fe-b73b-2fce75084966
2 February 2021
Singh, Iknoor
20f9c5b6-5d83-4583-afbe-118ac7cd61ba
P, Deepak
1c9443bb-3a4f-49c7-b42f-b978ae5b2a11
Kadan, Anoop
9cc17e26-a329-49fe-b73b-2fce75084966
Singh, Iknoor, P, Deepak and Kadan, Anoop
(2021)
On the coherence of fake news articles.
European Conference on Machine Learning and Knowledge Discovery in Databases: International Workshop on News Recommendation and Analytics, , Ghent, Belgium.
14 - 18 Sep 2020.
.
(doi:10.1007/978-3-030-65965-3_42).
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Conference or Workshop Item
(Paper)
Abstract
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.
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Published date: 2 February 2021
Venue - Dates:
European Conference on Machine Learning and Knowledge Discovery in Databases: International Workshop on News Recommendation and Analytics, , Ghent, Belgium, 2020-09-14 - 2020-09-18
Keywords:
Fake News Detection
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Local EPrints ID: 495939
URI: http://eprints.soton.ac.uk/id/eprint/495939
PURE UUID: 6d02457e-4003-45fe-8e64-fcf16578feb2
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Date deposited: 27 Nov 2024 17:59
Last modified: 28 Nov 2024 03:09
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Author:
Iknoor Singh
Author:
Deepak P
Author:
Anoop Kadan
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