Characterizing and Predicting Fake News Spreaders in Social Networks
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.
Shrestha, Anu and Spezzano, Francesca. (2022). "Characterizing and Predicting Fake News Spreaders in Social Networks". International Journal of Data Science and Analytics, 13(4), 385-298. https://doi.org/10.1007/s41060-021-00291-z