Stanford Psychology Podcast
131 - Johannes Eichstaedt: Is Social Media to Blame for Mental Illness? (REAIR)
Apr 25, 2024
Dr. Johannes Eichstaedt discusses using social media to understand mental illnesses like depression, the challenges of predicting rarer disorders, exploring user demographics on social media, using phone sensors for depression prediction, and the clinical applications of big data indicators in mental health diagnosis. The conversation also covers the complexities of using social media data for mental health analysis, privacy issues, and the impact of social media use on different demographics.
47:16
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Quick takeaways
- Social media can serve as a tool to predict mental illness like depression based on users' posts.
- Research focuses on detecting depression due to its prevalence and the challenge of predicting rarer conditions.
Deep dives
Using Social Media to Predict Depression
The podcast episode explores Dr. Johannes Echtag's research on using social media to detect markers of depression. By analyzing what individuals post on social media and combining it with medical records, the study found predictive signals of depression, such as markers of low mood and rumination, before an official diagnosis. This approach offers a potential early warning system for identifying individuals at risk of depression.
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