TY - JOUR
T1 - Detection of Aggressive and Violent Incidents from Social Media in Spanish using Pre-trained Language Model
AU - Tonja, Atnafu Lambebo
AU - Arif, Muhammad
AU - Kolesnikova, Olga
AU - Gelbukh, Alexander
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2022
Y1 - 2022
N2 - Violent and several other related problems, such as aggressive speech, offensive language, or bullying, are experiencing a growing online presence in the context of contemporary social media platforms. The research efforts towards detecting, isolating, and stopping these disturbing behaviors have intensified, in tight relation to the increasing performance of deep learning techniques applied in various Natural Language Processing (NLP) tasks. This paper present the Instituto Politécnico Nacional, Centro de Investigación en Computación (CIC) team's system description paper for shared task @IberLEF2022. This study explores the applicability of language-specific pre-trained language model for tackling the problem of detection of aggressive and violent incidents from social media in Spanish for DA-VINCIS:@IberLEF2022 shared task. The proposed model on the DA-VINCIS dataset achieves F1 score of 0.7455 for violent event identification task (Task 1) and F1-score 0.4903 for violent event category recognition (Task 2).
AB - Violent and several other related problems, such as aggressive speech, offensive language, or bullying, are experiencing a growing online presence in the context of contemporary social media platforms. The research efforts towards detecting, isolating, and stopping these disturbing behaviors have intensified, in tight relation to the increasing performance of deep learning techniques applied in various Natural Language Processing (NLP) tasks. This paper present the Instituto Politécnico Nacional, Centro de Investigación en Computación (CIC) team's system description paper for shared task @IberLEF2022. This study explores the applicability of language-specific pre-trained language model for tackling the problem of detection of aggressive and violent incidents from social media in Spanish for DA-VINCIS:@IberLEF2022 shared task. The proposed model on the DA-VINCIS dataset achieves F1 score of 0.7455 for violent event identification task (Task 1) and F1-score 0.4903 for violent event category recognition (Task 2).
KW - Aggressive incidents
KW - DistilBETO
KW - Social media
KW - Spanish aggressive incident
KW - Spanish violent incident
KW - Violent incidents
UR - http://www.scopus.com/inward/record.url?scp=85137379803&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85137379803
SN - 1613-0073
VL - 3202
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2022 Iberian Languages Evaluation Forum, IberLEF 2022
Y2 - 20 September 2022
ER -