BiDETECT: BiLSTM with BERT for Hate Speech Detection in Tweets

Alagu Prakalya P.*, Nirmal Gaud**
* Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India.
** Department of Computer Science and Engineering, VIT Bhopal, Madhya Pradesh, India.
Periodicity:January - March'2023
DOI : https://doi.org/10.26634/jcom.10.4.19334

Abstract

The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.

Keywords

Hate Speech, Deep Learning, BiDETECT, BERT, BiLSTM, Social Media.

How to Cite this Article?

Prakalya, P. A. and Gaud, N. (2023). BiDETECT: BiLSTM with BERT for Hate Speech Detection in Tweets. i-manager’s Journal on Computer Science, 10(4), 23-32. https://doi.org/10.26634/jcom.10.4.19334

References

[10]. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 1-9.
[14]. Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532-1543).
[16]. Waseem, Z., & Hovy, D. (2016, June). Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In Proceedings of the NAACL Student Research Workshop (pp. 88-93).
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.