Communications of the ACM
Technical Perspective: Leveraging Social Context for Fake News Detection
As social media emerged as a key source of information, malicious users started to manipulate social platforms to their own ends. Today, online disinformation efforts (so-called infodemics) are routinely plaguing public debates, political events, and information campaigns alike. Detecting fake news via online social media has become a central issue, fostering an arms race between malicious users and platform operators.
To this day, two broad strategies have been developed to automatically detect disinformation campaigns on online media: analyzing the information content—leveraging natural language processing techniques3 or authoritative information sources—or analyzing its context, for example by exploring the interplay between end users, publishers, and news pieces.5 In the following paper, the authors focus on the latter strategy by introducing a new graph-based, contextual technique for fake news detection. Their approach is based on two main pillars: a structurally rich graph representation of social context on one hand, and a dedicated learning framework leveraging an inductive approach to graph representation learning on the other hand.
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