用Transformer模型识别针对LGBTQ+群体的网络欺凌，提升精准度。
Detecting LGBTQ+ Instances of Cyberbullying
- 对比多种Transformer模型在识别LGBTQ+网络欺凌上的表现。
- 基于真实社交媒体数据验证模型有效性，发现性能差异显著。
- 适合关注数字安全与社群保护的研究者和平台方使用。
社交媒体持续影响人类发展轨迹，但其普及也使键盘成为攻击工具，将现实中的欺凌行为转移到线上，即网络欺凌。网络欺凌对全球青少年构成重大威胁，严重损害心理健康。其中，LGBTQ+群体尤为脆弱，研究已证实该群体在线上遭受更大程度的骚扰。因此，开发能实时准确识别针对LGBTQ+成员的网络欺凌的机器学习模型至关重要。本研究旨在比较多种Transformer模型在识别针对LGBTQ+个体的网络欺凌事件中的有效性，通过真实社交媒体数据评估这些现有方法在应对复杂且微妙的网络欺凌形式时的表现优劣。
原文摘要 · Abstract (English)
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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