Deep One-Class Hate Speech Detection Model

Saugata Bose, Dr. Guoxin Su


Abstract
Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detection classifier is trained with hate-class samples only. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with four benchmarking datasets demonstrates the better performance of our model than existing approaches, as well as the advantage of training our model with a combination of the four datasets.
Anthology ID:
2022.lrec-1.761
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
7040–7048
Language:
URL:
https://aclanthology.org/2022.lrec-1.761
DOI:
Bibkey:
Cite (ACL):
Saugata Bose and Dr. Guoxin Su. 2022. Deep One-Class Hate Speech Detection Model. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7040–7048, Marseille, France. European Language Resources Association.
Cite (Informal):
Deep One-Class Hate Speech Detection Model (Bose & Su, LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.761.pdf
Data
Hate Speech