mlsanityData to 14 Oct 2026

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

arXiv 2105.14888 ↗First posted 31 May 2021cs.CLcited by 14

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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.

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

15 of 51 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2018Deep contextualized word representationscited by 2,290
  4. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 407
  5. 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 271
  6. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  7. 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 196
  8. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  9. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 84
  10. 2018Conversations Gone Awry: Detecting Early Signs of Conversational Failurecited by 73
  11. 2020Multilingual Offensive Language Identification with Cross-lingual Embeddingscited by 71
  12. 2017Word Affect Intensitiescited by 51
3 more
  1. 2020Offensive Language Identification in Greekcited by 50
  2. 2021MUDES: Multilingual Detection of Offensive Spanscited by 30
  3. 2018Challenges in Discriminating Profanity from Hate Speechcited by 23

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Cited by year

Citing papers per year: 2021: 5; 2022: 2; 2023: 2; 2024: 3; 2025: 2.

14 papers.

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  1. 2021Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathicited by 28
  2. 2021Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTcited by 16
  3. 2021Sequence-to-Sequence Lexical Normalization with Multilingual Transformerscited by 7
  4. 2024On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlookcited by 6
  5. 2025Datasets for Depression Modeling in Social Media: An Overviewcited by 5
  6. 2021A Psychologically Informed Part-of-Speech Analysis of Depression in Social Mediacited by 4
  7. 2022The State of Profanity Obfuscation in Natural Language Processingcited by 4
  8. 2021WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Commentscited by 3
  9. 2023A Text-to-Text Model for Multilingual Offensive Language Identificationcited by 3
  10. 2022Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depressioncited by 1
  11. 2023Depression detection in social media posts using affective and social norm featurescited by 1
  12. 2024Towards Generalized Offensive Language Identificationcited by 1
2 more
  1. 2024Modeling offensive content detection for TikTokcited by 0
  2. 2025ViToSA: Audio-Based Toxic Spans Detection on Vietnamese Speech Utterancescited by 0