Vocabulary-based Method for Quantifying Controversy in Social Media
This addresses the need for efficient and language-agnostic controversy detection to improve online discussions and reduce information segregation, representing a domain-specific advancement.
The paper tackled the problem of detecting controversial topics in social media by developing a vocabulary-based method that uses jargon, and found it outperforms state-of-the-art graph-based measures in experiments across multiple languages and contexts.
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.