Globally, more than four billion people use social media, generating huge stores of data from their devices. That information can be used in tracking more than just what they buy, their political leanings or the patterns of social media usage during the pandemic. It can also be channeled to help better detect mental illness and improve well-being.
A growing number of studies show that language patterns and images in posts can reveal and predict mental health conditions for individuals and also evaluate mental health trends across entire populations.
Thanks to advances in artificial intelligence, natural language processing and other data science tools, researchers, tech companies, government agencies and nongovernmental organizations can make use of these gargantuan databases to look for signs of mental health conditions, such as depression, anxiety and suicide risk.
In certain countries, Facebook’s online suicide prevention program uses AI to scan users’ posts for images and words that may identify a person who may have a tendency toward self-harm. A team of trained human reviewers is alerted to posts that display patterns of suicidal thoughts and sends out mental health resources to at-risk users. In serious cases, emergency services may be notified to warn of imminent risk for self-harm.
Pinterest’s “compassionate search” connects users seeking information on anxiety and other mental-health-related topics with links that promote emotional wellness, including...
Read Full Story: https://www.scientificamerican.com/article/mining-social-media-reveals-mental-health-trends-and-helps-prevent-self-harm/
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