SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction [preprint]

Preprint date

February 9, 2021

Authors

Bhavtosh Rath (Ph.D. student), Xavier Morales, Jaideep Srivastava (professor)

Abstract

False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node's neighborhood using historical behavioral data and network structure and explain how features of a spreader's neighborhood vary. Using real world Twitter datasets, we show that the model is able to predict false information spreaders with an accuracy of over 87%.

Link to full paper

SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction

Keywords

data mining, social media