@inproceedings{guo-gauch-2024-using,
    title = "Using Sarcasm to Improve Cyberbullying Detection",
    author = "Guo, Xiaoyu  and
      Gauch, Susan",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Malmasi, Shervin  and
      Chakravarthi, Bharathi Raja  and
      Lahiri, Bornini  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the Fourth Workshop on Threat, Aggression {\&} Cyberbullying @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.trac-1.7",
    pages = "52--59",
    abstract = "Cyberbullying has become more prevalent over time, especially towards minority groups, and online human moderators cannot detect cyberbullying content efficiently. Prior work has addressed this problem by detecting cyberbullying with deep learning approaches. In this project, we compare several BERT-based benchmark methods for cyberbullying detection and do a failure analysis to see where the model fails to correctly identify cyberbullying. We find that many falsely classified texts are sarcastic, so we propose a method to mitigate the false classifications by incorporating neural network-based sarcasm detection. We define a simple multilayer perceptron (MLP) that incorpo- rates sarcasm detection in the final cyberbully classifications and demonstrate improvement over benchmark methods.",
}

Cyberbullying has become more prevalent over time, especially towards minority groups, and online human moderators cannot detect cyberbullying content efficiently. Prior work has addressed this problem by detecting cyberbullying with deep learning approaches. In this project, we compare several BERT-based benchmark methods for cyberbullying detection and do a failure analysis to see where the model fails to correctly identify cyberbullying. We find that many falsely classified texts are sarcastic, so we propose a method to mitigate the false classifications by incorporating neural network-based sarcasm detection. We define a simple multilayer perceptron (MLP) that incorpo- rates sarcasm detection in the final cyberbully classifications and demonstrate improvement over benchmark methods.
Guo, Xiaoyu Gauch, Susan
Using Sarcasm to Improve Cyberbullying Detection