Understanding the effects of psychiatric medication during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many suffer from a lack of generalizability in large populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Based on a propensity score based causal analysis, we observe that usage of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes, and post-treatment linguistic markers correlated with positive out- comes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.
The SocWeB Lab's mission is to develop novel computational techniques, and technologies powered by these techniques, to responsibly and ethically employ social media in quantifying, understanding, and improving our mental health and well-being.
