Near Real-Time Detection of Misinformation on Online Social Networks

Abstract

In this paper, we focus on the automatic detection of misinformation articles on online social networks. We study micro-blog posts that propagate news articles and classify these articles as misinformation or trusted information. We do this by extracting a comprehensive set of network and linguistic features and propose a deep learning model that combines both feature types. Experiments on real data demonstrate that our proposed method detects misinformation with an accuracy of 93% in near-real time. Moreover, we compare network and linguistic features with respect to the earliness of detection and combine these features with temporal information about diffusion patterns. We find that combining both feature types is optimal for the detection of misinformation articles in near-real time.