A Framework for Controversial Political Topics Identification Using Twitter Data - Sakiyama, Kenzo; Rodrigues, Lucas de Souza; Nogueira, Bruno Magalhães; Matsubara, Edson Takashi; Romero, Roseli A. F. (Anais da Brazilian Conference on Intelligent Systems (BRACIS), 2023)
Social networks have become the main stage for discussion on various current topics. In particular, electoral processes tend to bring many publications with polarized opinions on political issues addressed by candidates. A comprehensive analysis of social media publications on high-impact controversial topics and the opinions expressed in them could contribute to a clearer understanding of the dynamics of political discussion, providing valuable insights for society. In this context, we investigate how to apply a clustering-based topic modeling approach to produce public evaluation information on different current issues, in particular controversial political topics. We propose a framework that enriches text representations, combining state-of-the-art unsupervised (HDBSCAN) and supervised (BERTimbau) techniques to identify controversial political topics in social media publications in Brazilian Portuguese. To this end, weekly collections were carried out on the social network Twitter, making it possible to identify controversial events for each analyzed date. We compare the controversial topics uncovered with real-world news to validate the results and compare our method with a traditional method described in the literature.