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Detecting Fake News Spreaders on Twitter from a Multilingual Perspective

 
: Vogel, Inna; Meghana, Meghana

:

Webb, G. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020. Proceedings : 6-9 October 2020, Sydney, Australia
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2020
ISBN: 978-1-7281-8206-3
ISBN: 978-1-7281-8207-0
pp.599-606
International Conference on Data Science and Advanced Analytics (DSAA) <7, 2020, Online>
English
Conference Paper
Fraunhofer SIT ()

Abstract
The creators of fake news often use facts from verified news sources and layer them with misinformation to confuse the reader, either intentionally or unintentionally. It can be increasingly seen as a threat to democracy, public order and free debate that can cause confusion and provoke unrest. Several websites have taken on the mission of fact-checking rumors and claims - particularly those that get thousands of views and likes before being debunked and dismissed by expert sources. To prevent fake news from being spread among online users, a near real-time reaction is crucial. Fact-checking websites are often not fast enough to verify the content of all the news being spread. Fake news detection is a challenging task aiming to reduce human time and effort to check the truthfulness of news. In this paper, we propose an approach that is able to identify possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users. Therefore, we conduct different learning experiments from a multilingual perspective, English and Spanish. We evaluate different textual features that are primarily not tied to a specific language and compare different machine learning algorithms. Our results indicate that language-independent features can be used to distinguish between possible fake news spreaders and users who share credible information with an average detection accuracy of 78% for the English and 87% for the Spanish corpus.

: http://publica.fraunhofer.de/documents/N-621523.html