Towards cyberbullying detection : building, benchmarking and longitudinal analysis of aggressiveness and conflicts/attacks datasets from Twitter

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dc.contributor.authorFerreira, Paula
dc.contributor.authorPereira, Nadia
dc.contributor.authorRosa, Hugo
dc.contributor.authorOliveira, Sofia
dc.contributor.authorCoheur, Luisa
dc.contributor.authorFrancisco, Sofia
dc.contributor.authorSouza, Sidclay
dc.contributor.authorRibeiro, Ricardo
dc.contributor.authorCarvalho, Joao P.
dc.contributor.authorPaulino, Paula
dc.contributor.authorTrancoso, Isabel
dc.contributor.authorVeiga-Simao, Ana Margarida
dc.contributor.institutionHEI-LAB - Human Environment Interaction Lab
dc.date.accessioned2025-06-18T15:50:01Z
dc.date.available2025-06-18T15:50:01Z
dc.date.issued2024-12-16
dc.descriptionPublisher Copyright: © 2010-2012 IEEE.
dc.description.abstractOffense 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.en
dc.description.sponsorshipThis work received national funding from FCT – Fundação para a Ciência e a Tecnologia, I.P., through the Research Center for Psychological Science of the Faculty of Psychology, University of Lisbon (PTDC/MHC/PED/3297/2014; PTDC/PSI-GER/1918/2020; UIDB/04527/2020; UIDP/04527/2020), and in collaboration with INESC-ID via project reference UIDB/50021/2020.
dc.identifier.citationFerreira, P, Pereira, N, Rosa, H, Oliveira, S, Coheur, L, Francisco, S, Souza, S, Ribeiro, R, Carvalho, J P, Paulino, P, Trancoso, I & Veiga-Simao, A M 2024, 'Towards cyberbullying detection : building, benchmarking and longitudinal analysis of aggressiveness and conflicts/attacks datasets from Twitter', IEEE Transactions on Affective Computing, vol. 16, no. 3, pp. 1-15. https://doi.org/10.1109/TAFFC.2024.3518587
dc.identifier.doihttps://doi.org/10.1109/TAFFC.2024.3518587
dc.identifier.issn1949-3045
dc.identifier.urihttp://hdl.handle.net/10437/15394
dc.identifier.urlhttps://www.scopus.com/pages/publications/85212842471
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Affective Computing
dc.rightsopenAccess
dc.subjectCOMPUTER SCIENCE
dc.subjectCYBERBULLYING
dc.subjectDATASETS
dc.subjectHATE SPEECH
dc.subjectNATURAL LANGUAGE PROCESSING
dc.subjectSOCIAL NETWORKS
dc.subjectINFORMÁTICA
dc.subjectCYBERBULLYING
dc.subjectDADOS
dc.subjectDISCURSOS DE ÓDIO
dc.subjectLINGUAGEM NATURAL
dc.subjectREDES SOCIAIS
dc.subjectULHT/HEI-Lab - Artigos de Revistas Internacionais com Arbitragem Científica
dc.titleTowards cyberbullying detection : building, benchmarking and longitudinal analysis of aggressiveness and conflicts/attacks datasets from Twitteren
dc.typearticle

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