Characterizing and Predicting Fake News Spreaders in Social Networks

Anu Shrestha, Francesca Spezzano

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Due to its rapid spread over social media and the societal threat of changing public opinion, fake news has gained massive attention. Users’ role in disseminating fake news has become inevitable with the increase in popularity of social media for daily news diet. People in social media actively participate in the creation and propagation of news, favoring the proliferation of fake news intentionally or unintentionally. Thus, it is necessary to identify the users who tend to share fake news to mitigate the rampant dissemination of fake news over social media. In this article, we perform a comprehensive analysis on two different datasets collected from Twitter and investigate the patterns of user characteristics in social media in the presence of misinformation. Specifically, we study the correlation between the user characteristics and their likelihood of being fake news spreaders and demonstrate the potential of the proposed features in identifying fake news spreaders. Our proposed approach achieves an average precision ranging between 0.80 and 0.99 on the considered datasets, consistently outperforming baseline models. Furthermore, we also show that the user personality traits, emotions, and writing style are strong predictors of fake news spreaders.

Original languageAmerican English
Pages (from-to)385-398
Number of pages14
JournalInternational Journal of Data Science and Analytics
Volume13
Issue number4
DOIs
StatePublished - May 2022

Keywords

  • Fake news spreaders
  • Misinformation
  • User characterization
  • User classification

EGS Disciplines

  • Computer Sciences

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