Quantifying and Reducing Controversy in Social Media

Quantifying and Reducing Controversy in Social Media


Gianmarco De Francisci Morales


Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers i.e., situations where like-minded people reinforce each other’s opinion, but do not get exposed to the views of the opposing side. In the first part of this presentation, I will show a systematic methodological study of controversy detection using social-media network structure and content. In the second part, I will follow up by presenting a method for bridging these chambers, and thus reduce controversy. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph. Then, we study algorithmic techniques for bridging these echo chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph as a whole. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph.

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