USING ARTIFICIAL INTELLIGENCE AND SOCIAL MEDIA DATA FOR POPULATION MENTAL HEALTH SURVEILLANCE

Digital technologies have transformed how individuals express psychological distress, seek support, and interact with mental health information. Advances in artificial intelligence (AI) have enabled the analysis of large scale unstructured data sources such as social media, search queries, and digital text to detect early signals of mental health risk at the population level. This plenary will examine how computational psychiatry and digital mental health methods can complement traditional clinical models by enabling real time surveillance of depression, anxiety, and substance use. Drawing on empirical examples from large scale social media and multimodal data studies, this talk will highlight how natural language processing, computer vision, and machine learning can identify patterns of distress, risk escalation, and exposure to harmful content before individuals present to clinical care. The presentation will also address ethical considerations including privacy, algorithmic harm, representativeness, and the risk of reinforcing stigma or inequities through digital systems.

Learning Objective 1: Describe how artificial intelligence methods applied to social media and digital text data can be used to monitor population level mental health risks in real time.

Learning Objective 2: Evaluate the ethical, clinical, and equity considerations involved in integrating digital mental health surveillance tools into psychiatric research and practice.

References

Chancellor, S., and De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine, 3(1), 43. Insel, T. R. (2017). Digital phenotyping: technology for a new science of behavior. Jama, 318(13), 1215-1216.