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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.761.pdf
- Data
- Hate Speech