# Social Media for Mental Health: Data, Methods, and Findings

arXiv: https://arxiv.org/abs/2511.07914 · HTML: https://mlsanity.com/paper/2511.07914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)
- [Quantifying Mental Health from Social Media with Neural User Embeddings](https://mlsanity.com/paper/1705.00335/) (2017)
- [Identifying Depression on Twitter](https://mlsanity.com/paper/1607.07384/) (2016)
- [Triaging Content Severity in Online Mental Health Forums](https://mlsanity.com/paper/1702.06875/) (2017)
- [The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia](https://mlsanity.com/paper/1804.03507/) (2018)
- [Gender-Based Violence in 140 Characters or Fewer: A #BigData Case Study of Twitter](https://mlsanity.com/paper/1503.02086/) (2015)
- [Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning](https://mlsanity.com/paper/1708.01372/) (2017)

## Abstract (arXiv)

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.