Multimodal Framework for Hate Speech Recognition Using BERT and Deep Learning Architectures

Abstract

Hate speech recognition is a challenge in Automatic Speech Recognition systems which is used to recognize the unwanted text, audio, which is been generated by the various social media platforms. In the recent study Bidirectional Encoder Representational Transformer (BERT) model and Natural language processing (NLP) used to build a model which is used to recognize the hate speech, audio, and process that hated content. In the previous work this learning used only for the text but in this project, it used for both audios. In this paper we implemented a hate speech recognition model which are used to recognize the hated content more accurately with an accuracy of 94%.

Introduction

This paper addresses the growing problem of online hate speech (HS), a form of abusive content targeting individuals based on attributes like race, religion, gender, or nationality. The widespread use of social media has amplified the spread of HS, prompting the need for effective, real-time detection techniques. The study focuses on leveraging deep learning (DL) and transfer learning, particularly using the BERT model, to classify toxic content more accurately and efficiently.

Key Points:

1. Problem Overview

• Hate speech has increased globally with the rise of social media and meme culture (e.g., 180 million meme posts in 2018).

• HS includes text, speech, images, and video, but most research focuses on text-based detection using NLP techniques.

• Detecting hate speech is complex—false positives can harm free speech, while false negatives maintain toxic environments.

2. Proposed Solution

• The paper introduces a multimodal deep learning architecture for classifying hate speech, using BERT (Bidirectional Encoder Representations from Transformers).

• BERT provides context-aware vector embeddings of text, enhancing classification accuracy for offensive language.

• Transfer learning is employed: the model is pre-trained on massive corpora and fine-tuned on labeled hate speech datasets (e.g., tweets).

3. Methodology

• Transformer Models (BERT & mBERT): Used to capture nuanced contextual meaning from text.

• Transfer Learning: Reduces the need for large labeled datasets by adapting pre-trained language models to the hate speech task.

• Neural Networks:

  • CNNs identify local text patterns (e.g., offensive word sequences).

  • LSTMs handle longer dependencies in text sequences.

• Classification Task: BERT was fine-tuned to classify content into categories like Racism, Sexism, Hate, Offensive, or Neither.

4. Results

• The BERT-based model achieved ~94% accuracy, outperforming traditional machine learning models like SVM.

• It demonstrates strong generalization even with limited annotated datasets due to effective transfer learning.

• Embedding fine-tuning and architectural tuning (e.g., CNN + BERT) significantly improved detection performance.

5. Related Work

• Previous studies used RNNs, CNNs, and traditional ML models (e.g., SVM), but lacked the context sensitivity of transformer models.

• Some studies visualized neural network attention regions and feature saliency to better understand hate speech triggers.

Conclusion

This project successfully demonstrates leveraging pre-trained models like BERT for hate speech detection across both textual and audio modalities. The findings shows that combination of audio and textual features enhances the accuracy and robustness of the hatespeech recognition.The proposed framework based on transfer learning and deep learning neural network offers a promising solution for real time and scalable moderation of abusive online content with an accuracy of 94%.Future directions will be directed in detecting hatespeech in video transcriptions.

References

[1] Kulsoom, F., Narejo, S., Mehmood, Z. et al. A review of machine learning-based human activity recognition for diverse applications. Neural Comput & Applic 34, 18289–18324 (2022). https://doi.org/10.1007/s00521-022-07665-9. [2] Narejo, Sanam et al. ‘Big Data Analytics and Classification of Cardiovascular Disease Using Machine Learning’. 1 Jan. 2022 : 2025– 2033. [3] Khan, Wisal & Turab, Muhammad & Ahmad, Waqas & Ahmad, Syed & Kumar, Kelash & Luo, Bin. (2022). Data Dimension Reduction makes ML Algorithms efficient. 10.48550/arXiv.2211.09392. [4] Sarwar, Savera, et al. \"Advanced Audio Aid for Blind People.\" 2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC). IEEE, 2022. [5] Khan, Wisal, et al. \"Data Dimension Reduction makes ML Algorithms efficient.\" 2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC). IEEE, 2022. [6] Kumar, Teerath, et al. \"Forged character detection datasets: passports, driving licences and visa stickers.\" Int. J. Artif. Intell. Appl.(IJAIA) 13 (2022). [7] P. Mishra, M. D. Tredici, H. Yannakoudakis, and E. Shutova, “Abusive Language Detection with Graph Convolutional Networks,” in NAACL, 2019. [8] J. Pavlopoulos, P. Malakasiotis, and I. Androutsopoulos, “Deep Learningfor User Comment Moderation,” in Proceedings of the First Workshop on Abusive Language Online. Vancouver, BC, Canada: Association for Computational Linguistics, August 2017, pp. 25–35. [Online]. Available: 10.18653/v1/W17- 3004 [9] Turab, Muhammad & Jamil, Sonain. (2023). A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse. BioMedInformatics. 3. 563-584. 10.3390/biomedinformatics3030039. [10] SMD SHAFIULLA(2023). A review on Natural Language Processing techniques using Qualitative Research,363-367. [11] Marzieh Mozafari, Reza Farahbakhsh, and Noel Crespi,2019, A BERT-Based Transfer Learning Approach for ,Hate Speech Detection in Online Social Media,

Copyright

Copyright © 2025 SMD Shafiulla, L. Shiva Kumar, N. Manideep, S. Varshith Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.