Summary of Machine Learning Approaches For Mental Illness Detection on Social Media: a Systematic Review Of Biases and Methodological Challenges, by Yuchen Cao et al.
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
by Yuchen Cao, Jianglai Dai, Zhongyan Wang, Yeyubei Zhang, Xiaorui Shen, Yunchong Liu, Yexin Tian
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a systematic review of machine learning models that detect mental illness, specifically depression, using social media data. It identifies biases and methodological challenges in the machine learning lifecycle and evaluates 47 studies published after 2010 for their methodological quality and risk of bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how machine learning can help identify mental health issues like depression on social media. Researchers collected data from many different studies that used this approach, looked at what worked well and what didn’t, and found some common problems that need to be fixed. By doing this research, we can develop better ways to use social media to detect mental illness and get people the help they need sooner. |
Keywords
* Artificial intelligence * Machine learning
