Looking Beyond Content: Modeling and Detection of Fake News from a Social Context Perspective

Date
2022-01-04
Authors
Xiao, Kenan
Wang, Longwei
Gupta, Ashish
Qin, Xiao
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Abstract
The widespread fake news on social media has boosted the demand for reliable fake news detection techniques. Such dissemination of fake news can influence public opinions and society. More recently, a growing number of methods for detecting fake news have been proposed. However, most of these approaches have significant limitations in timely detection of fake news. To facilitate early detection of fake news, we propose a unique framework FNEPP (Fake News Engagement and Propagation Path) from a social context perspective, which explicitly combines news contents, user engagements, user characteristics, and the news propagation path as composite features of two collaborative modules. The engagement module captures news contents and user engagements, while the propagation path module learns global and local patterns of user characteristics and news dissemination patterns. Experimental results on two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework.
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Data, Text, and Web Mining for Business Analytics, deep learning, fake news detection, social media analysis
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