Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/25973
Author(s): Santos, R. B.
Matos, B. C.
Carvalho, P.
Batista, F.
Ribeiro, R.
Editor: Cordeiro, J., Pereira, M. J., Rodrigues, N. F., and Pais, S.
Publication date/Defense date: 2022
Title: Semi-supervised annotation of Portuguese hate speech across social media domains
Volume: 104
Book title/volume: OpenAccess Series in Informatics
Event title: 11th Symposium on Languages, Applications and Technologies (SLATE 2022)
ISSN: 2190-6807
ISBN: 978-3-95977-245-7
DOI (Digital Object Identifier): 10.4230/OASIcs.SLATE.2022.11
Keywords: Hate speech
Semi-supervised learning
Semi-automatic annotation
Abstract: With the increasing spread of hate speech (HS) on social media, it becomes urgent to develop models that can help detecting it automatically. Typically, such models require large-scale annotated corpora, which are still scarce in languages such as Portuguese. However, creating manually annotated corpora is a very expensive and time-consuming task. To address this problem, we propose an ensemble of two semi-supervised models that can be used to automatically create a corpus representative of online hate speech in Portuguese. The first model combines Generative Adversarial Networks and a BERT-based model. The second model is based on label propagation, and consists of propagating labels from existing annotated corpora to the unlabeled data, by exploring the notion of similarity. We have explored the annotations of three existing corpora (CO-HATE, ToLR-BR, and HPHS) in order to automatically annotate FIGHT, a corpus composed of geolocated tweets produced in the Portuguese territory. Through the process of selecting the best model and the corresponding setup, we have tested different pre-trained embeddings, performed experiments using different training subsets, labeled by different annotators with different perspectives, and performed several experiments with active learning. Furthermore, this work explores back translation as a mean to automatically generate additional hate speech samples. The best results were achieved by combining all the labeled datasets, obtaining 0.664 F1-score for the Hate Speech class in FIGHT.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:IT-CRI - Comunicações a conferências internacionais

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