Natural Language Processing Techniques and Methods for Identifying Negative Speech in Social Media

Shumin Wang

doi:10.54254/2755-2721/2026.TJ29656

Applied and Computational EngineeringOpen access

Natural Language Processing Techniques and Methods for Identifying Negative Speech in Social Media

Research Article

Open Access

Natural Language Processing Techniques and Methods for Identifying Negative Speech in Social Media

Shumin Wang ^1*

¹ Taiyuan University of Technology

^*Corresponding author: 485134416@qq.com

Published on 19 November 2025

ACE Vol.207

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-539-4

ISBN (Online): 978-1-80590-540-0

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Abstract

With the increasingly prominent role of social media in information dissemination and social interaction, its anonymity and openness have also led to the gradual prominence of negative speech, which has adverse effects on individual psychology and social stability. Traditional manual moderation models struggle to meet practical needs due to limitations like the vast volume of negative speech and the potential psychological harm to moderators. With automation, real-time processing, and scalability, Natural Language Processing (NLP) serves as a crucial tool for identifying negative language. This paper examines the concept, characteristics, and categories of negative speech on social media, offering a comprehensive analysis of the NLP frameworks employed in negative speech detection, including methods for text representation, feature extraction, and the practical applications of various models. Through a review of existing studies, this paper highlights key optimizations in various methods, identifies substantial limitations and issues in current technologies, and presents key findings on future development trends, informing research and applications in negative speech detection on social media.

Keywords:

Social Media, Negative Speech Identification, Natural Language Processing (NLP), Pre-trained Models, Deep Learning

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References

[1]. Saleous, H., Gergely, M., & Shuaib, K. (2025). Exploring NLP-based solutions to social media moderation challenges. Wiley Human Behavior and Emerging Technologies, 9436490..

[2]. Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022). Building towards automated cyberbullying detection: A comparative analysis. Hindawi Computational Intelligence and Neuroscience, 4794227.

[3]. Dogra, V., Verma, S., Kavita, P., Chatterjee, P., Shafi, J., Choi, J., & Ijaz, M. F. (2022). A complete process of text classification system using state-of-the-art NLP models. Computational Intelligence and Neuroscience, 1883698.

[4]. Liu, P., Li, W., & Zou, L. (2019). NULI at SemEval-2019 task 6: Transfer learning for offensive language detection using bidirectional transformers. Proceedings of SemEval-2019 Task 6, 87-91.

[5]. Caselli, T., Basive, V., Mitrovic, J., et al. (2020). HateBERT: Retraining BERT for abusive language detection in English, 4-5.

[6]. Riyadi, S., Andriyani, A. D. Y., & Sulaiman, S. N. (2024). Improving hate speech detection using double-layer hybrid CNN-RNN model on imbalanced dataset. IEEE Access, 12, 159660-159668.

[7]. Yadav, U., Bondre, S., Thakre, B., et al. (2024). Speech-to-text Emotion Detection System using SVM, CNN, and BERT. In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE), 1-5.

[8]. Sharma, D., Gupta, V., & Singh, V. (2023). Detection of homophobia & transphobia in Dravidian languages: Exploring deep learning methods. In International Conference on Advanced Network Technologies and Intelligent Computing, 1798, 225-236.

[9]. Li, Z.Q. (2023). A Study on Cross-Domain Multimodal Hate Speech Detection. Dalian University of Technology.

References

[1]. Saleous, H., Gergely, M., & Shuaib, K. (2025). Exploring NLP-based solutions to social media moderation challenges. Wiley Human Behavior and Emerging Technologies, 9436490..

[4]. Liu, P., Li, W., & Zou, L. (2019). NULI at SemEval-2019 task 6: Transfer learning for offensive language detection using bidirectional transformers. Proceedings of SemEval-2019 Task 6, 87-91.

[5]. Caselli, T., Basive, V., Mitrovic, J., et al. (2020). HateBERT: Retraining BERT for abusive language detection in English, 4-5.

[6]. Riyadi, S., Andriyani, A. D. Y., & Sulaiman, S. N. (2024). Improving hate speech detection using double-layer hybrid CNN-RNN model on imbalanced dataset. IEEE Access, 12, 159660-159668.

[9]. Li, Z.Q. (2023). A Study on Cross-Domain Multimodal Hate Speech Detection. Dalian University of Technology.

Cite this article

Wang,S. (2025). Natural Language Processing Techniques and Methods for Identifying Negative Speech in Social Media. Applied and Computational Engineering,207,76-82.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-539-4(Print) / 978-1-80590-540-0(Online)

Editor: Marwan Omar, Guozheng Rao

Conference website: https://www.confspml.org/tianjin.html

Conference date: 21 December 2025

Series: Applied and Computational Engineering

Volume number: Vol.207

ISSN: 2755-2721(Print) / 2755-273X(Online)

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