License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SLATE.2022.10
URN: urn:nbn:de:0030-drops-167560
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16756/
Matos, Bernardo Cunha ;
Santos, Raquel Bento ;
Carvalho, Paula ;
Ribeiro, Ricardo ;
Batista, Fernando
Comparing Different Approaches for Detecting Hate Speech in Online Portuguese Comments
Abstract
Online Hate Speech (OHS) has been growing dramatically on social media, which has motivated researchers to develop a diversity of methods for its automated detection. However, the detection of OHS in Portuguese is still little studied. To fill this gap, we explored different models that proved to be successful in the literature to address this task. In particular, we have explored transfer learning approaches, based on existing BERT-like pre-trained models. The performed experiments were based on CO-HATE, a corpus of YouTube comments posted by the Portuguese online community that was manually labeled by different annotators. Among other categories, those comments were labeled regarding the presence of hate speech and the type of hate speech, specifically overt and covert hate speech. We have assessed the impact of using annotations from different annotators on the performance of such models. In addition, we have analyzed the impact of distinguishing overt and and covert hate speech. The results achieved show the importance of considering the annotator’s profile in the development of hate speech detection models. Regarding the hate speech type, the results obtained do not allow to make any conclusion on what type is easier to detect. Finally, we show that pre-processing does not seem to have a significant impact on the performance of this specific task.
BibTeX - Entry
@InProceedings{matos_et_al:OASIcs.SLATE.2022.10,
author = {Matos, Bernardo Cunha and Santos, Raquel Bento and Carvalho, Paula and Ribeiro, Ricardo and Batista, Fernando},
title = {{Comparing Different Approaches for Detecting Hate Speech in Online Portuguese Comments}},
booktitle = {11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
pages = {10:1--10:12},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-245-7},
ISSN = {2190-6807},
year = {2022},
volume = {104},
editor = {Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/16756},
URN = {urn:nbn:de:0030-drops-167560},
doi = {10.4230/OASIcs.SLATE.2022.10},
annote = {Keywords: Hate Speech, Text Classification, Transfer Learning, Supervised Learning, Deep Learning}
}
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Keywords: |
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Hate Speech, Text Classification, Transfer Learning, Supervised Learning, Deep Learning |
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Collection: |
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11th Symposium on Languages, Applications and Technologies (SLATE 2022) |
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Issue Date: |
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2022 |
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Date of publication: |
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27.07.2022 |
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Supplementary Material: |
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Software (Code, Data and Models): https://hate-covid.inesc-id.pt/?page_id=200 |
