Towards Cyberbullying Detection: Building, Benchmarking and Longitudinal Analysis of Aggressiveness and Conflicts/Attacks Datasets from Twitter

• Autor(a) principal: Ferreira, Paula
• Data de Publicação: 2024
• Outros Autores: Salgado Pereira, Nádia, Rosa, Hugo, Oliveira, Sofia, Coheur, Luísa, Francisco, Sofia, Souza, Sidclay B., Ribeiro, Ricardo, Carvalho, João P., Paulino, Paula, Trancoso, Isabel, Veiga Simão, Ana
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10400.5/98143
• topic: Aggression; Offense; Hate speech; Social networks; Natural language processing; Dataset

Offense and hate speech are a source of online conflicts which have become common in social media and, as such, their study is a growing topic of research in machine learning and natural language processing. This article presents two Portuguese language offense-related datasets that deepen the study of the subject: an Aggressiveness dataset and a Conflicts/Attacks dataset. While the former is similar to other offense detection related datasets, the latter constitutes a novelty due to the use of the history of the interaction between users. Several studies were carried out to construct and analyze the data in the datasets. The first study included gathering expressions of verbal aggression witnessed by adolescents to guide data extraction for the datasets. The second study included extracting data from Twitter (in Portuguese) that matched the most frequent expressions/words/sentences that were identified in the previous study. The third study consisted in the development of the Aggressiveness dataset, the Conflicts/Attacks dataset, and classification models. In our fourth study, we proposed to examine whether online aggression and conflicts/attacks revealed any trend changes over time with a sample of 86 adolescents. With this study, we also proposed to investigate whether the amount of tweets sent over a period of 273 days was related to online aggression and conflicts/attacks. Lastly, we analyzed the percentage of participants who participated in the aggressions and/or attacks/conflicts.