February 17, 2026 by David Bradley, Inderscience
Collected at: https://techxplore.com/news/2026-02-ai-social-media-language-flag.html
Mental health problems are among the most pressing of public health challenges, affecting millions across different age groups and societies. Depression, anxiety, and stress-related conditions rank among the leading causes of diminished quality of life worldwide. They exact a heavy social toll and economic cost. Yet diagnosis still relies largely on self-reported symptoms and intermittent clinical interviews, which means diagnosis is vulnerable to memory lapse, stigma, and limited access to trained professionals.
Research published in the International Journal of Networking and Virtual Organisations discusses an artificial intelligence (AI) diagnostic system that can spot early signs of various mental health conditions by analyzing how people write online. The model, known as a Fossa-based graph neural network (FbGNN), examines language patterns in text drawn from social media platforms and online forums. Instead of relying solely on questionnaires, it studies sentiment-driven textual information, the emotional tone, word choices and behavioral cues embedded in a person’s online writing.
The researchers explain that their system combines two advanced computational techniques. The first is the Fossa optimization, a feature-selection method based on search strategies seen in nature. In machine learning, features are identifiable pieces of information, specific words, phrases or emotional markers. By applying Fossa optimization, the system can filter out any irrelevant data from those features and identify pertinent indicators of mental distress.
The second component is a graph neural network. A GNN analyzes relationships by representing information as a network of nodes and connections. Here, nodes correspond to features, and the connections are the interactions between them. This allows the model to detect complex patterns, such as recurring combinations of emotional expression and behavioral signals.
By training the system to classify text based on categories such as depression, anxiety, stress, bipolar disorder, suicidal ideation, and personality disorders, the team was able to then test its accuracy against known sample data. It was able to predict a person’s mental health status with an accuracy of almost 99% in the trials.
Such accuracy would be useful in screening for mental health problems among a cohort of users, such as students, employees, or any other group. It would allow health care follow-ups to be directed at those most likely to have problems that might be addressed and would only miss one in a hundred. Further refinements of the system could bring that accuracy closer to 100%.
More information: G. Sherlin Shobitha et al, A smart intelligent Internet of Things framework for predicting mental health, International Journal of Networking and Virtual Organisations (2025). DOI: 10.1504/ijnvo.2025.151510
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