# Predicting mental health using social media: A roadmap for future development

arXiv: https://arxiv.org/abs/2301.10453 · HTML: https://mlsanity.com/paper/2301.10453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 130 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology](https://mlsanity.com/paper/1809.03006/) (2018)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step](https://mlsanity.com/paper/1510.04221/) (2015)
- [Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons](https://mlsanity.com/paper/1411.0778/) (2014)
- [A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo](https://mlsanity.com/paper/2008.11708/) (2020)

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

- [Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks](https://mlsanity.com/paper/2306.16891/) (2023)

## Abstract (arXiv)

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. The considerable number of social media users and the tremendous quantity of user-generated data on social platforms provide a unique opportunity for researchers to distinguish patterns that correlate with mental status. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques. We describe the common approaches for predicting and identifying the disorder using user-generated content. This research is organized according to the data collection, feature extraction, and prediction algorithms. Furthermore, we review several recent studies conducted to explore different features of candidate profiles and their analytical methods. Following, we debate various aspects of the development of experimental auto-detection frameworks for identifying users who suffer from disorders, and we conclude with a discussion of future trends. The introduced methods can help complement screening procedures, identify at-risk people through social media monitoring on a large scale, and make disorders easier to treat in the future.