Year: 2024
Type: article
Abstract: In recent years, we have seen a dramatic increase in the use of social media in our daily lives.Due to user privacy and convenience of use, the prevalence of hate speech spread on social media platforms has greatly increased.To safeguard their users and society, social media platforms need to censor and prevent the spread of hate speech on their platforms.Due to the enormous amount of traffic on these platforms, automatic detection of hate speech is required.Hate speech is one of the most difficult categorization tasks in text mining.The use of machine learning algorithms such as support vector machines, logistic regression, convolutional neural networks, K-nearest neighbor algorithm, and random forest have been the focus of research on this topic.The employment of ensemble techniques has also been used.However, the results of these approaches are indeed unsatisfactory.In our work, the genetic programming approach for binary text classification is proposed to recognize hate speech in a given dataset.The results of the applied experiments show the effectiveness of our genetic programming strategy in text classification.Moreover, the proposed approach outperformed the state-of-the-art when employing the same dataset, which emphasizes the capability of genetic programming techniques in text classification.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409604857