Spreader-Centric Fake News Mitigation Framework Based on Epidemiology

• Published in: Disease Control Through Social Network Surveillance pp. 31 - 54
• Main Authors: Rath, Bhavtosh, Srivastava, Jaideep
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Social Networks
• Subjects: Epidemiology; Fake news mitigation; Social network analysis
• ISBN: 3031078683; 9783031078682
• ISSN: 2190-5428; 2190-5436
• DOI: 10.1007/978-3-031-07869-9_2

Computational models for the detection and prevention of false information spreading (popularly called fake news) has gained a lot of attention over the last decade, with most proposed models identifying the veracity of information. In this chapter we propose a framework based on a complementary approach to false information mitigation inspired from the domain of Epidemiology, where false information is analogous to infection, social network is analogous to population and likelihood of people believing an information is analogous to their vulnerability to infection. As part of the framework we propose four phases that fall in the domain of social network analysis. Through experiments on real world information spreading networks on Twitter, we show the effectiveness of our models and confirm our hypothesis that spreading of false information is more sensitive to behavioral properties like trust and credibility than spreading of true information.