# Detecting LGBTQ+ Instances of Cyberbullying

arXiv: https://arxiv.org/abs/2409.12263 · HTML: https://mlsanity.com/paper/2409.12263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments](https://mlsanity.com/paper/2109.00227/) (2021)
- [Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study](https://mlsanity.com/paper/1812.08046/) (2018)
- [Detection of Cyberbullying Incidents on the Instagram Social Network](https://mlsanity.com/paper/1503.03909/) (2015)
- [Examining the Unique Online Risk Experiences and Mental Health Outcomes of LGBTQ+ versus Heterosexual Youth](https://mlsanity.com/paper/2402.08974/) (2024)

## Built on by (most cited first, 1 of 1)

- [Advancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets](https://mlsanity.com/paper/2509.02908/) (2025)

## Abstract (arXiv)

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.