# Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

arXiv: https://arxiv.org/abs/2202.03291 · HTML: https://mlsanity.com/paper/2202.03291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams](https://mlsanity.com/paper/1905.08772/) (2019)
- [Mining social media data for biomedical signals and health-related behavior](https://mlsanity.com/paper/2001.10285/) (2020)
- [How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters](https://mlsanity.com/paper/1804.02318/) (2018)
- [Causal Effects of Brevity on Style and Success in Social Media](https://mlsanity.com/paper/1909.02565/) (2019)
- [Venue Suggestion Using Social-Centric Scores](https://mlsanity.com/paper/1803.08354/) (2018)

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

- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)

## Abstract (arXiv)

Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions' co-occurrence patterns, and online behavioural traits, including social engagement and posting trends. Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.