No abstract

Nowadays controversial topics on social media are often linked to hate speeches, fake news propagation, and biased or misinformation spreading. Detecting controversy in online discussions is a challenging task, but essential to stop these unhealthy behaviours.In this work, we develop a general pipeline to quantify controversy on social media through content analysis, and we widely test it on Twitter.Our approach can be outlined in four phases: an initial graph building phase, a community identification phase through graph partitioning, an embedding phase, using language models, and a final controversy score computation phase. We obtain an index that quantifies the intuitive notion of controversy.To test that our method is general and not domain-, language-, geography-or size-dependent, we collect, clean and analyze 30 Twitter datasets about different topics, half controversial and half not, changing domains and magnitudes, in six different languages from all over the world.The results confirm that our pipeline can quantify correctly the notion of controversy, reaching a ROC AUC score of 0.996 over controversial and non-controversial scores distributions. It outperforms the state-of-the-art approaches, both in terms of accuracy and computational speed.

Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply. We perform an extensive set of experiments across many languages, regions and contexts, taking controversial and non-controversial topics. We find that our vocabulary-based measure performs better than state of the art measures that are based only on the community graph structure. Moreover, we shows that it is possible to detect polarization through text analysis.