Using Artificial Intelligence Algorithms to Detect Hate Speech in Social Media Posts

Abstract

Detecting hate speech on social media is of great importance to prevent negative impacts on people and communities and to remove such content. However, detecting hate speech is a complex and challenging process due to linguistic and cultural diversity. Therefore, it is important to develop powerful and effective machine learning algorithms. Since detecting such content using traditional methods can be time-consuming and costly, it is stated that artificial intelligence-based machine learning algorithms have great potential in this regard. The aim of this study is to evaluate the performance of artificial intelligence-based machine learning algorithms used in detecting posts containing hate speech on social media. The study focuses on the problem of detecting and managing hate speech on social media platforms. In this study, we will compare the performances of different algorithms and determine the most suitable methods. Additionally, the effects of the dataset and feature extraction methods on algorithm performance will be analyzed. Algorithms are often based on natural language processing techniques and try to detect hate speech by learning features in texts. The performance of these algorithms can vary depending on factors such as language, culture, the attributes they use, and the training dataset, so a comprehensive analysis is required. In the research, the performance of the algorithms used in detecting hate speech was compared with the dataset and feature extraction methods. In this process, the algorithms' linguistic and cross-cultural effectiveness, feature selection and representation, false positive and false negative rates, and overall accuracy will be analyzed.

Keywords

• Social Media
• Hate Speech
• Artificial Intelligence

References

1. Kaplan, A.M. and Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.
2. Allport, G. W. (1954). The nature of prejudice. Cambridge, MA: Addison-Wesley.
3. Perry, B. (2001). In the name of hate: Understanding hate crimes. New York: Routledge.
4. Chetty, N., and Alathur, S. (2018). Hate speech review in the context of online social networks. Aggression and Violent Behavior, 40, 108-118.
5. Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International Conference on Web and social media, ICWSM 2017, pp. 512-515.
6. Schmidt, A., and Wiegand, M. (2017). A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for social media (pp. 1-10).
7. Fortuna, P., and Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4), 1-30.
8. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (pp. 1137-1143). Morgan Kaufmann.

9. Bergstra, J., and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(Feb), 281-305.
10. Sokolova, M., and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427-437.