TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Detection of twitter hate tweets leading to crime using multiseries BERT model

* ,

* Corresponding author · click or hover a name for details

pp. 885–896Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1613XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1613
Pages:
885–896

Abstract

Addressing hate speech on social media platforms is a pressing concern, particularly on platforms like Twitter and Facebook. To date, extensive research and numerous projects on the subject of hate speech detection and prevention have been conducted, as well as for crime detection and its prevention. However, there is a major void in the current research terrain whereby there exists no specific effort to study how hate speech and crime are related globally. This research work proposes a multi-series methodology to investigate the complex linkages between online hate speech and real world acts of violence. Multi series BERT model from transformer was used for detecting the hate speech in the tweets of popular politicians, celebrities, peoples tweets on popular cases and showing the linkage of these to the real world crime happenings. When compared with the other available popular models like CNN. KNN, SVM, RF, LR, NB, DT which are generally used for detecting hate speech and crime detection, the proposed BERT model gave great results with 93% in accuracy as well as 92% in F1 Score outperforming all the other models. Furthermore the results showed that states like Delhi, Maharashtra and Tamil Nadu have the higher chances of crime happening due to hate speech based on the prediction done. However the data may vary for different social media platforms as the current research work is only based on twitter.

Keywords

Subject Classifications

68T4568T5068T20

References

[1] Agarwal, Pushkal, et al. “Hate speech in political discourse: A case study of UK MPs on Twitter.” Proceedings of the 32nd ACM conference on hypertext and social media (2021).
[2] Wang, Chih-Chien, Min-Yuh Day, and Chun-Lian Wu. “Political Hate Speech Detection and Lexicon Building: A Study in Taiwan.” IEEE Access 10 : 44337-44346 (2022).
[3] Velankar, Abhishek, Hrushikesh Patil, and Raviraj Joshi. “A review of challenges in machine learning based automated hate speech detection” (2022). arXiv preprint arXiv:2209.05294.
[4] Koushik, Garima, K. Rajeswari, and Suresh Kannan Muthusamy. “Automated hate speech detection on Twitter.” 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE (2019).
[5] Waseem, Zeerak, and Dirk Hovy. “Hateful symbols or hateful people? predictive features for hate speech detection on twitter.” Proceedings of the NAACL student research workshop (2016).
[6] Mullah, Nanlir Sallau, and Wan Mohd Nazmee Wan Zainon. “Advances in machine learning algorithms for hate speech detection in social media: a review.” IEEE Access 9 : 88364-88376 (2021).
[7] Abro, Sindhu, et al. “Automatic hate speech detection using machine learning: A comparative study.” International Journal of Advanced Computer Science and Applications 11.8 (2020).
[8] Elisabeth, Damayanti, Indra Budi, and Muhammad Okky Ibrohim. “Hate code detection in Indonesian tweets using machine learning approach: a dataset and preliminary study.” 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE (2020).
[9] Watanabe, Hajime, Mondher Bouazizi, and Tomoaki Ohtsuki. “Hate speech on twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection.” IEEE access 6 : 13825-13835 (2018).
[10] Mody, Akankshi, et al. “Identification of potential cyber bullying tweets using hybrid approach in sentiment analysis.” 2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE (2018).
[11 ] Alrehili, Ahlam. “Automatic hate speech detection on social media: A brief survey.” 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE (2019). 
[12] Putri, T. T. A., et al. “A comparison of classification algorithms for hate speech detection.” Iop conference series: Materials science and engineering. Vol. 830. No. 3. IOP Publishing (2020).
[13] Oriola, Oluwafemi, and Eduan Kotzé. “Evaluating machine learning techniques for detecting offensive and hate speech in South African tweets.” IEEE Access 8 : 21496-21509 (2020). 
[14] Alkomah, Fatimah, and Xiaogang Ma. “A literature review of textual hate speech detection methods and datasets.” Information 13.6 : 273 (2022). 
[15] Alshalan, Raghad, and Hend Al-Khalifa. “A deep learning approach for automatic hate speech detection in the saudi twittersphere.” Applied Sciences 10.23 : 8614 (2020).
[16] Boishakhi, Fariha Tahosin, Ponkoj Chandra Shill, and Md Golam Rabiul Alam. “Multi-modal hate speech detection using machine learning.” 2021 IEEE International Conference on Big Data (Big Data). IEEE (2021).
[17] Khan, Shakir, et al. “HCovBi-caps: hate speech detection using convolutional and Bi-directional gated recurrent unit with Capsule network.” IEEE Access 10 : 7881-7894 (2022).
[18] Roy, Pradeep Kumar, et al. “A framework for hate speech detection using deep convolutional neural network.” IEEE Access 8 : 204951-204962 (2020).
[19] Pérez, Juan Manuel, et al. “Assessing the impact of contextual information in hate speech detection.” IEEE Access 11 : 30575-30590 (2023).
[20] Pérez, Juan Manuel, et al. “Assessing the impact of contextual information in hate speech detection.” IEEE Access 11 : 30575-30590 (2023).
[21] Mozafari, Marzieh, Reza Farahbakhsh, and Noel Crespi. “Cross-lingual few-shot hate speech and offensive language detection using meta learning.” IEEE Access 10 : 14880-14896 (2022).
[22] Sharmila, P., et al. “PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation.” IEEE Access 10 : 105366-105376 (2022).
[23] Davidson, Thomas, et al. “Automated hate speech detection and the problem of offensive language.” Proceedings of the international AAAI conference on web and social media. Vol. 11. No. 1 (2017).
[24] Malik, Jitendra Singh, Guansong Pang, and Anton van den Hengel. “Deep learning for hate speech detection: a comparative study” (2022). arXiv preprint arXiv:2202.09517.
[25] Vaswani, Ashish, et al. “Attention is all you need.” Advances in neural information processing systems 30 (2017).
[26] Vivek, Meghashyam, and Boppuru Rudra Prathap. “Crime Analysis and Forecasting using Twitter Data in the Indian Context.” 2023 International Conference on Artificial Intelligence and Smart Communication (AISC). IEEE (2023).
[27] Nayak, S. R., Arora, V., Sinha, U., & Poonia, R. C. A statistical analysis of COVID-19 using Gaussian and probabilistic model. Journal of Interdisciplinary Mathematics, 24(1), 19–32 (2021).
[28] Khandelwal, Sonal & Chaudhary, Aanyaa. COVID-19 pandemic & cyber security issues: Sentiment analysis and topic modeling approach, Journal of Discrete Mathematical Sciences and Cryptography, 25:4, 987-997 (2022).
[29] Teng, Yin-Pei & Lan, Li-Zhen. Investor sentiment at the initial stage of public emergency: Evidence from Shanghai stock market, Journal of Statistics and Management Systems, 27:1, 1–8 (2024).

Views: 217Downloads: 119Citations: 0