Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content. 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 threestage 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. We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the ltering and aggregation of social media content for disseminating news stories. In this paper, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media.Unlike previous work, rather than study controversy in a single hand-picked topic and use domain-speci c knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph.We perform an extensive comparison of controversy measures, di erent graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We nd that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

Controversial issues often split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of "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 this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent discussions as graphs, and cast our objective as an edge-recommendation problem. The goal of the recommendation is to reduce the controversy score of the graph, measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edges, which represent the probability that the recommended edges materialize in the graph. * This is an abridged version of a homonymous paper that received the best student paper award in ACM WSDM 2017.