Automatic controversy detection in social media: A content-independent motif-based approach

In this work, we show that it is possible to detect controversy in social media by exploiting network motifs, i.e., local patterns of user interaction. The proposed approach allows for a language-independent and fine-grained analysis of user discussions and their evolution over time. Network motifs can be easily extracted both from user interactions and from the underlying social network, and they are conceptually simple to define and very efficient to compute. We assess the predictive power of motifs on a manually labeled twitter dataset. In fact, a supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features. Finally, thanks to the locality of motif patterns, we show that it is possible to monitor the evolution of controversy in a conversation over time thus discovering changes in user opinion.