Investigating User Information and Social Media Features in Cyberbullying Detection
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
As society grows increasingly more online with each passing year, the problem of cyberbullying becomes more and more prominent, with such incidents having the capacity to negatively impact mental health in a major way, especially among children and teenagers. The proposed approach builds on our previous work that established multi-modal detection of cyberbullying on Twitter, and restructures the multi-modal approach by incorporating social media features such as time-related features and social network information. As a result, the new models reach a classification accuracy between 94.4% and 94.6%, from the previous accuracy of 93%. The proposed new approach affirms the use of context-based data in addition to more directly-related features when analyzing cyberbullying and other interactions with promising improvements. We believe that this work contributes significantly to the study of cyberbullying detection, which is an imminent problem with growing importance in the post-COVID society.
machine learning, natural language processing, neural networks, sentiment analysis
Jiabao Qiu, Nihar Hegde, Melody Moh, and Teng Sheng Moh. "Investigating User Information and Social Media Features in Cyberbullying Detection" Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (2022): 3063-3070. https://doi.org/10.1109/BigData55660.2022.10020305