Published September 14, 2023
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Detecting Hate Speech In X(Twitter) Using Sentiment Analysis
Description
Accurately separating hate speech from offensive language is difficult for computerized hate-speech detection on social media. Currently used techniques frequently lack accuracy, and supervised learning is ineffective at successfully differentiating between these categories. This study collects tweets with hate speech keywords and, using a crowd-sourced hate speech lexicon, labels them as either hate speech, offensive language, or neither. A multi-class classifier is trained to distinguish between these categories, showing instances where it is more challenging to distinguish between hate speech and offensive language. The study discovered its classification of tweets as hate speech was less probable for those containing homophobic or racist rhetoric in contrast to those espousing sexist or racist perspectives, which were more likely to receive such designation. Additionally, tweets without clear hateful language provide a bigger classification challenge.
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References
- Pinkesh Badjatiya , Shashank Gupta , Manish Gupta , Vasudeva Varma1, "Deep Learning for Hate Speech Detection in Tweets," in IW3C2 , pp. 759–760, April 2017, doi: 10.1145/3041021.3054223 2. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. "Automated Hate Speech Detection and the Problem of Offensive Language". Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (May 3, 2017): 512-515. doi: 10.1609/icwsm.v11i1.14955. 3. Y. Zhang, W. Wang, and X. He. "BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs." arXiv preprint arXiv:1704.06125 (2017). 4. Isobelle Clarke and Jack Grieve. 2017. Dimensions of Abusive Language on Twitter. In Proceedings of the First Workshop on Abusive Language Online, pages 1–10, Vancouver, BC, Canada. Association for Computational Linguistics. 5. Burnap, P., and Williams, M. L. 2015. Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making. Policy & Internet 7(2):223– 242 ISSN: 2455-2631 September 2023 IJSDR | Volume 8 Issue 9 IJSDR2309085 International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org 585 6. K. Balci and A. A. Salah. 2015. Automatic analysis and identification of verbal aggression and abusive behaviors for online social games. Computers in Human Behavior 53:517–526 7. Burnap, P., & Williams, M. L. (2016, March 23). Us and them: identifying cyber hate on Twitter across multiple protected characteristics - EPJ Data Science. SpringerOpen. https://doi.org/10.1140/epjds/s13688-016-0072-6 8. Papers with Code - Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media. (n.d.). Expressively Vulgar: The Socio-dynamics of Vulgarity and Its Effects on Sentiment Analysis in Social Media | Papers With Code. https://paperswithcode.com/paper/expressively-vulgar-the-socio-dynamics-of 9. in Cheif, I. E. (n.d.). Welcome to IJSDR UGC CARE norms ugc approved journal norms IJRTI Research Journal | ISSN : 2455-2631. Welcome to IJSDR UGC CARE Norms Ugc Approved Journal Norms IJRTI Research Journal | ISSN : 2455- 2631. https://www.ijsdr.org/ 10. Wankhade, Mayur, Annavarapu Chandra Sekhara Rao, and Chaitanya Kulkarni. "A survey on sentiment analysis methods, applications, and challenges." Artificial Intelligence Review 55.7 (2022): 5731-5780. 11. Zhang, Wenxuan, et al. "A survey on aspect-based sentiment analysis: Tasks, methods, and challenges." IEEE Transactions on Knowledge and Data Engineering (2022). 12. Huang, Bo, et al. "Aspect-level sentiment analysis with aspect-specific context position information." Knowledge-Based Systems 243 (2022): 108473. 13. He, Lu, Tingjue Yin, and Kai Zheng. "They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets." Journal of Biomedical Informatics 132 (2022): 104142. 14. Mao, Rui, et al. "The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection." IEEE Transactions on Affective Computing (2022). 15. Pavitha, N., et al. "Movie recommendation and sentiment analysis using machine learning." Global Transitions Proceedings 3.1 (2022): 279-284. 16. Pavitha, N., et al. "Movie recommendation and sentiment analysis using machine learning." Global Transitions Proceedings 3.1 (2022): 279-284. 17. Zhu, Tong, et al. "Multimodal sentiment analysis with image-text interaction network." IEEE Transactions on Multimedia (2022). 18. Babu, Nirmal Varghese, and E. Grace Mary Kanaga. "Sentiment analysis in social media data for depression detection using artificial intelligence: a review." SN Computer Science 3 (2022): 1-20. 19. Feng, Zijian, et al. "Tailored text augmentation for sentiment analysis." Expert Systems with Applications 205 (2022): 117605. 20. Tokarchuk, Oksana, Jacob Charles Barr, and Claudia Cozzio. "How much is too much? Estimating tourism carrying capacity in urban context using sentiment analysis." Tourism Management 91 (2022): 104522