AI analysis of social media posts for depression signs, Digi Mart USA News: People often share highly personal details on social media over many years, including writing about sleep problems, lack of motivation, anxiety, self-doubt and their moods. According to a report from Notebookcheck, researchers at George Mason University have developed an AI-based method that analyzes these posts to identify potential signs of depression.
The system does not simply flag posts that sound sad or use negative language. It is designed to avoid misclassification, so a person who writes that a friend is suffering from depression should not automatically be treated as depressed themselves.
The method works through several steps to filter content for relevance before assessing it.
Compared with previous methods, the model improved precision, meaning the share of retrieved results that are actually relevant, by a relative 70 percent. However, the actual precision rose only from 10 to 17 percent.
The F1 score, which balances how many retrieved results are relevant with how many relevant results the AI identifies, reached 27 percent. The report notes that these scores remain relatively low, though the approach represents a meaningful improvement over earlier techniques.
The study also shows that large volumes of social media posts can be filtered efficiently to find content relevant to complex tasks such as detecting symptoms of depression. Researchers have not yet demonstrated reliable identification of people with depression, and the report states the method is not currently suitable for diagnostic purposes.
The findings also highlight a privacy concern: personal information can still be extracted from posts written years ago, including details users may not remember sharing.
