# Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals

arXiv: https://arxiv.org/abs/2207.10032 · HTML: https://mlsanity.com/paper/2207.10032/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 54 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://mlsanity.com/paper/1906.04571/) (2019)
- [RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models](https://mlsanity.com/paper/2106.03521/) (2021)
- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Does Gender Matter? Towards Fairness in Dialogue Systems](https://mlsanity.com/paper/1910.10486/) (2019)
- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [On Cross-Dataset Generalization in Automatic Detection of Online Abuse](https://mlsanity.com/paper/2010.07414/) (2020)
- [Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments](https://mlsanity.com/paper/2109.00227/) (2021)
- [Controlled Analyses of Social Biases in Wikipedia Bios](https://mlsanity.com/paper/2101.00078/) (2020)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [Detect All Abuse! Toward Universal Abusive Language Detection Models](https://mlsanity.com/paper/2010.03776/) (2020)
- [Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management](https://mlsanity.com/paper/2110.09271/) (2021)
- [What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020](https://mlsanity.com/paper/2109.12192/) (2021)
- [Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English](https://mlsanity.com/paper/2206.08978/) (2022)

## Built on by (most cited first, 8 of 8)

- ["I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation](https://mlsanity.com/paper/2305.09941/) (2023)
- [MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification](https://mlsanity.com/paper/2409.14703/) (2024)
- [Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data](https://mlsanity.com/paper/2212.10440/) (2022)
- [Advancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets](https://mlsanity.com/paper/2509.02908/) (2025)
- [Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Society](https://mlsanity.com/paper/2309.09450/) (2023)
- [Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using Language Models](https://mlsanity.com/paper/2501.13948/) (2025)
- [SLAyiNG: A Diverse and Community-validated Dataset of Queer Slang](https://mlsanity.com/paper/2509.17449/) (2025)
- [QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Tasks](https://mlsanity.com/paper/2601.20731/) (2026)

## Abstract (arXiv)

Online discussions, panels, talk page edits, etc., often contain harmful conversational content i.e., hate speech, death threats and offensive language, especially towards certain demographic groups. For example, individuals who identify as members of the LGBTQIA+ community and/or BIPOC (Black, Indigenous, People of Color) are at higher risk for abuse and harassment online. In this work, we first introduce a real-world dataset that will enable us to study and understand harmful online conversational content. Then, we conduct several exploratory data analysis experiments to gain deeper insights from the dataset. We later describe our approach for detecting harmful online Anti-LGBTQIA+ conversational content, and finally, we implement two baseline machine learning models (i.e., Support Vector Machine and Logistic Regression), and fine-tune 3 pre-trained large language models (BERT, RoBERTa, and HateBERT). Our findings verify that large language models can achieve very promising performance on detecting online Anti-LGBTQIA+ conversational content detection tasks.