# An Exploratory Analysis of the Relation Between Offensive Language and Mental Health

arXiv: https://arxiv.org/abs/2105.14888 · HTML: https://mlsanity.com/paper/2105.14888/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Challenges in Discriminating Profanity from Hate Speech](https://mlsanity.com/paper/1803.05495/) (2018)

## Built on by (most cited first, 14 of 14)

- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media](https://mlsanity.com/paper/2108.00279/) (2021)
- [The State of Profanity Obfuscation in Natural Language Processing](https://mlsanity.com/paper/2210.07595/) (2022)
- [WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments](https://mlsanity.com/paper/2108.00057/) (2021)
- [A Text-to-Text Model for Multilingual Offensive Language Identification](https://mlsanity.com/paper/2312.03379/) (2023)
- [Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression](https://mlsanity.com/paper/2204.13569/) (2022)
- [Depression detection in social media posts using affective and social norm features](https://mlsanity.com/paper/2303.14279/) (2023)
- [Towards Generalized Offensive Language Identification](https://mlsanity.com/paper/2407.18738/) (2024)
- [Modeling offensive content detection for TikTok](https://mlsanity.com/paper/2408.16857/) (2024)
- [ViToSA: Audio-Based Toxic Spans Detection on Vietnamese Speech Utterances](https://mlsanity.com/paper/2506.00636/) (2025)

## Abstract (arXiv)

In this paper, we analyze the interplay between the use of offensive language and mental health. We acquired publicly available datasets created for offensive language identification and depression detection and we train computational models to compare the use of offensive language in social media posts written by groups of individuals with and without self-reported depression diagnosis. We also look at samples written by groups of individuals whose posts show signs of depression according to recent related studies. Our analysis indicates that offensive language is more frequently used in the samples written by individuals with self-reported depression as well as individuals showing signs of depression. The results discussed here open new avenues in research in politeness/offensiveness and mental health.