Detecting LGBTQ+ Instances of Cyberbullying

• URL: http://arxiv.org/abs/2409.12263v1
• Date: Wed, 18 Sep 2024 18:49:55 GMT
• Title: Detecting LGBTQ+ Instances of Cyberbullying
• Authors: Muhammad Arslan, Manuel Sandoval Madrigal, Mohammed Abuhamad, Deborah L. Hall, Yasin N. Silva,
• Abstract summary: Cyberbullying poses a significant threat to adolescents globally. The LGBTQ+ community is particularly at risk, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. It is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members.
• Score: 3.5723815685584013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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