A team of graduate student researchers led by Stony Brook University’s Andrew Schwartz, an assistant professor in the College of Engineering and Applied Science’s Department of Computer Science, and Stanford University’s Johannes Eichstaedt is using Twitter to track and analyze COVID-19 symptoms and mental health in U.S. communities.

Researchers say large-scale analysis of linguistic patterns in social media offer one of the few large-scale instruments for measuring the physical and psychological health of populations. The group also produces what seems to be the only county-level COVID time-tracker available.

The response to COVID-19 is said to be the largest psychological disruption of society since World War II, and the economic impact of unemployment creates additional distress.

The social networking site, Twitter, has been used in the past to track both communicable and non-communicable diseases.

The researchers can present more representative estimates through post-stratification by adjusting for demographic biases of the Twitter samples. By combining the nature of big social media data and the improvement of methods for inferring psychological and health information from it, a Twitter-based surveillance architecture can be a valuable tool to inform COVID-19-related public health decisions.

The research team is utilizing AI-based language assessment and statistical techniques to isolate dependable signals of active COVID-19 infections. A “sociolinguistic COVID-19 base rate” will be formed from the rate of these linguistic patterns in social media, controlled for general coronavirus trends in discussion.

Adding on to recently validated methods, the researchers are measuring the impact of the virus and of social distancing/shelter in place orders on mental health.