Epidemiology Inspired Framework for False Information Mitigation in Social Networks [thesis]

Author

Bhavtosh Rath (Ph.D. 2020)

Abstract

Social networking platforms like Facebook and Twitter are used by millions of people around the world to not only share information but also personal opinions about it. Often these information and opinions are unverified, which has caused the problem of spreading of false information, popularly termed Fake News. As social media platforms generate huge volumes of data, computational models for the detection and prevention of false information spreading has gained a lot of attention over the last decade, with most proposed models trying to identify the veracity of the information. Techniques involve extracting features from the information’s propagation path in social networks or from the information content itself. In this thesis we propose a complementary approach to false information mitigation inspired from the domain of epidemiology.

Link to full paper

Epidemiology Inspired Framework for False Information Mitigation in Social Networks

Keywords

data mining, social network analysis