What if Twitter knew someone was depressed 3 months before before a diagnosis?
Twitter users with depression and anxiety were found to be more likely to post pictures with lower aesthetic values and less vivid colors, particularly images in grayscale, according to a new study led by researchers at Penn Medicine. Additionally, they found that users tended to suppress positive emotions rather than outwardly display more negative emotions, such as keeping a straight face instead of outright frowning, in their profile pictures.
Researchers found that depression could be predicted as much as three months before a diagnosis by using artificial intelligence to identify keywords that flagged certain users.
Algorithms to extract features such as colors, facial expressions, and different aesthetic measures (such as depth of field, symmetry, and lighting) from images posted by more than 4,000 Twitter users who consented to be a part of the study. To quickly categorize their depression and anxiety scores, they analyzed each person's last 3,200 tweets. Meanwhile, 887 users also completed a traditional survey to obtain depression and anxiety scores. Then, image features were correlated with users' depression and anxiety scores. From this, several significant relationships emerged.
Depressed users often posted photos only of their own faces with no family, friends, or other people appearing in them. Additionally, the posts rarely included the recreational activities or interests, which more often showed up in photos of non-depressed users.
"This tool is far from perfect to be used as a diagnostic tool. However, an automated machine learning tool could be a low-cost method for clinicians, with permission from their patients, to monitor their accounts and potentially detect elevated depression or anxiety levels," Guntuku explained. "The clinicians could then refer patients who were flagged by tool for more formal screening methods."
How would you react if you received an automated tweet with depression self-help resources?
Source: https://www.sciencedaily.com/r.../05/190515115827.htm