This New AI Model Can Detect Mental Disorders Based On A Person’s Web Posts

Dartmouth researchers have built an artificial intelligence model for detecting mental disorders using conversations on Reddit. This tool analyzes social media posts and gains an insight into people’s mental states.

The model’s focus is on emotions rather than the specific content of the social media text. In a paper presented at the 20th International Conference on Web Intelligence and Intelligent Agent Technology (PDF), the researchers show that this approach performs better over time, irrespective of the topics discussed in the posts.

Usually, people don’t seek help for mental health disorders due to stigma, high costs, and lack of access to services are some common hindrances. “There is also a tendency to minimize signs of mental disorders or conflate them with stress”, says Xiaobo Guo, Guarini ’24, a co-author of the paper.

“Social media offers an easy way to tap into people’s behaviors,” says Guo. ”The data is voluntary and public, published for others to read”, he says.

Detecting Mental Disorders Based on Web Posts with AI – OpenGov Asia

Reddit offers a huge network of user forums. The posts and comments are publicly available, and the researchers could collect data dating back to 2011.

They have focused on major depressive, anxiety, and bipolar disorders which are characterized by distinct emotional patterns.

They have trained their model to label the emotions expressed in users’ posts and map the emotional transitions between different posts, so a post could be labeled ‘joy,’ ‘anger,’ ‘sadness,’ ‘fear,’ ‘no emotion,’ or a combination of these.

They create an emotional “fingerprint” for a user and compare it to established signatures of emotional disorders, the model can detect them.

Xiaobo Guo and Soroush Vosoughi

“This approach sidesteps an important problem called “information leakage” that typical screening tools run into”, says Soroush Vosoughi, assistant professor of computer science and another co-author.

For instance, if a model learns to correlate “COVID” with “sadness” or “anxiety,” Vosoughi explains, it will naturally assume that a scientist studying and posting (quite dispassionately) about COVID-19 is suffering from depression or anxiety.

 “It’s very important to have models that perform well,” says Vosoughi, “but also really understand their working, biases, and limitations.”

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