Cyberbullying Tweets Detection Within Twitter Using CNN
Y. Manasa, P. Dedeepya, Chinta Meghana Sai, Damerla Navya, Hari Priya Timmasarti, Meghana Gangavarapu · 2024
Social media has transformed into a prominent hub for cyberbullying, particularly impacting the younger demographic. The surge in social networking platforms has led to a corresponding increase in instances of online harassment. The primary objective is to employ machine learning techniques to detect linguistic patterns used by individuals involved in bullying within Twitter messages. By leveraging the Twitter API, which provides extensive access to public data, our goal is to develop a software solution capable of autonomously Recognize occurrences of bullying on the platform. The fusion of social science, computer science, and big data is crucial in overcoming the limitations associated with current methods of examining bullying. This approach seeks to advance our understanding of the issue through innovative interdisciplinary strategies. In future endeavors, our plan involves enhancing the accuracy of the existing Support Vector Machine (SVM) classifier by incorporating Convolutional Neural Networks (CNN), with the aim of achieving more promising and improved result.