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Stevens researchers use AI to detect depression

In March 2020, the world as we knew it changed in the blink of an eye. Social distancing was implemented and self-quarantine became the new normal. The pandemic has been challenging for many people. While some have been able to adjust well to the challenges of the pandemic, others are struggling. In August 2020, a report published by the CDC showed that 26% of adult survey respondents were experiencing symptoms of trauma and stress-related disorders, and 13% had increased substance use to cope with the pandemic. To this backdrop, a Stevens alumna and a professor teamed up to create an AI that could detect signs of depression in online text.

Rida Zainub ’20 and Dr. Rajarathnam Chandramouli, Hattrick Chair Professor of Electrical and Computer Engineering, conducted research that could change how depression is detected all over the world. According to Dr. Chandramouli, “depression is a serious global mental health issue. A severe shortage of mental health medical professionals especially in developing countries demands the research and development of technology solutions to address this problem.”

Their goal was to use natural language processing in combination with Explainable Artificial Intelligence to analyze text and detect signs of depression. They explain their method to solve this complex problem quite simply.

They started by collecting about 20,000 posts from Reddit, a platform where anonymity encourages users to write honestly about their feelings and where the text is normally longer compared to other social media platforms. The posts came from two sub-Reddits, /r/depression and /r/CasualConversation. Using an AI algorithm, they were able to identify linguistic features in the posts to determine which of the two sub-Reddits they came from, which served as a proxy for the state of the user’s mental health. Impressively, the algorithm was able to determine the source of the text almost 90% of the time. This process was applied in two different languages: English and Urdu.

When asked why they chose Urdu as the second language, Zainub replied, “we wanted to study how the understanding of depression and the linguistic context associated with depression might be different for people from a different cultural and lingual background. Since Urdu is the lingua franca in many parts of Pakistan and India and Urdu is my first language, it was only natural that we chose Urdu as part of our research.”

For the technology to work, they had to overcome many hurdles in the language interpretation of English and the Urdu translation. Using Google Translate and some manual labor, all the Urdu text was translated into English and run through the algorithm. However, given the limitations of Google Translate, even after manual revision, the translation was not perfect. An example of the type of error in translation is that the English words ‘I’ and ‘in’ are the same word in Urdu. This created a problem since the algorithm found the use of self-referential pronouns such as “I” to be important indicators of which sub-Reddit the text came from.

Dr. Chandramouli and Zainub concluded four major findings: (i) people tend to use personal pronouns more when they are depressed, (ii) people who are not depressed have a more diverse vocabulary, (iii) languages make a difference in the way people write their social media posts, and (iv) pre-processing data is important for getting accurate results.

Although the algorithm is not perfect and only limited to two languages, Zainub and Dr. Chandramouli hope to expand it in the future to include several more languages. They are also collaborating with psychiatrists to bring their AI tool to online therapy, which can be just as effective as in-person help while being more accessible and less time consuming.

The two researchers saw a huge void in mental health resources worldwide and built an innovative tool to help fill it. “Extending this research to other low resource languages will have a huge social impact,” said Dr. Chandramouli.

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