Detecting LGBTQ+ Instances of Cyberbullying
<|reference_start|>Detecting LGBTQ+ Instances of Cyberbullying: Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. The aim of this study is to compare the efficacy of several transformer models in identifying cyberbullying targeting LGBTQ+ individuals. We seek to determine the relative merits and demerits of these existing methods in addressing complex and subtle kinds of cyberbullying by assessing their effectiveness with real social media data.<|reference_end|>
@article{arslan2024detecting,
    title={Detecting LGBTQ+ Instances of Cyberbullying},
    author={Muhammad Arslan, Manuel Sandoval Madrigal, Mohammed Abuhamad, Deborah
  L. Hall, Yasin N. Silva},
    journal={arXiv preprint arXiv:2409.12263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12263},
    primaryClass={cs.LG cs.SI}
}