# Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments

arXiv: https://arxiv.org/abs/2109.00227 · HTML: https://mlsanity.com/paper/2109.00227/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 77 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [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)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [Generating Counter Narratives against Online Hate Speech: Data and Strategies](https://mlsanity.com/paper/2004.04216/) (2020)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Countering hate on social media: Large scale classification of hate and counter speech](https://mlsanity.com/paper/2006.01974/) (2020)
- [Multi-label Categorization of Accounts of Sexism using a Neural Framework](https://mlsanity.com/paper/1910.04602/) (2019)
- [Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language](https://mlsanity.com/paper/2103.10195/) (2021)
- [Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN](https://mlsanity.com/paper/1708.02420/) (2017)
- [Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?](https://mlsanity.com/paper/1911.01371/) (2019)

## Built on by (most cited first, 20 of 20)

- [Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals](https://mlsanity.com/paper/2207.10032/) (2022)
- [QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities](https://mlsanity.com/paper/2406.12399/) (2024)
- [bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments](https://mlsanity.com/paper/2203.14267/) (2022)
- [cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models](https://mlsanity.com/paper/2308.10370/) (2023)
- [Findings of the Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments](https://mlsanity.com/paper/2205.06118/) (2022)
- [MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments](https://mlsanity.com/paper/2401.14681/) (2024)
- [MuSeD: A Multimodal Spanish Dataset for Sexism Detection in Social Media Videos](https://mlsanity.com/paper/2504.11169/) (2025)
- [Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore's Low-Resource Languages](https://mlsanity.com/paper/2509.15260/) (2025)
- [Pretrained Transformers for Offensive Language Identification in Tanglish](https://mlsanity.com/paper/2110.02852/) (2021)
- [Detecting LGBTQ+ Instances of Cyberbullying](https://mlsanity.com/paper/2409.12263/) (2024)
- [Characterizing Network Structure of Anti-Trans Actors on TikTok](https://mlsanity.com/paper/2501.16507/) (2025)
- [QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Tasks](https://mlsanity.com/paper/2601.20731/) (2026)
- [Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text](https://mlsanity.com/paper/2111.09811/) (2021)
- [Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil](https://mlsanity.com/paper/2204.09087/) (2022)
- [Detection of Homophobia & Transphobia in Dravidian Languages: Exploring Deep Learning Methods](https://mlsanity.com/paper/2304.01241/) (2023)
- [cantnlp@LT-EDI-2024: Automatic Detection of Anti-LGBTQ+ Hate Speech in Under-resourced Languages](https://mlsanity.com/paper/2401.15777/) (2024)
- [Sociocultural Considerations in Monitoring Anti-LGBTQ+ Content on Social Media](https://mlsanity.com/paper/2407.01149/) (2024)
- [A Target-Aware Analysis of Data Augmentation for Hate Speech Detection](https://mlsanity.com/paper/2410.08053/) (2024)
- ["Is Hate Lost in Translation?": Evaluation of Multilingual LGBTQIA+ Hate Speech Detection](https://mlsanity.com/paper/2410.11230/) (2024)
- [MAAM: Anchor-Preserving Compression and Contextual Calibration for Chinese Discriminatory Language Detection](https://mlsanity.com/paper/2606.09114/) (2026)

## Abstract (arXiv)

The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as homophobia/transphobia. Homophobic/transphobic speech is a type of offensive language that may be summarized as hate speech directed toward LGBT+ people, and it has been a growing concern in recent years. Online homophobia/transphobia is a severe societal problem that can make online platforms poisonous and unwelcome to LGBT+ people while also attempting to eliminate equality, diversity, and inclusion. We provide a new hierarchical taxonomy for online homophobia and transphobia, as well as an expert-labelled dataset that will allow homophobic/transphobic content to be automatically identified. We educated annotators and supplied them with comprehensive annotation rules because this is a sensitive issue, and we previously discovered that untrained crowdsourcing annotators struggle with diagnosing homophobia due to cultural and other prejudices. The dataset comprises 15,141 annotated multilingual comments. This paper describes the process of building the dataset, qualitative analysis of data, and inter-annotator agreement. In addition, we create baseline models for the dataset. To the best of our knowledge, our dataset is the first such dataset created. Warning: This paper contains explicit statements of homophobia, transphobia, stereotypes which may be distressing to some readers.