Tracking Mental Health During the Coronavirus Pandemic

Alona Fyshe (Amii Fellow and Canada CIFAR AI Chair) has teamed up with Rumi Chunara (New York University), and Daniel Lizotte and Brent Davis (Western University) to leverage machine learning and social media to better understand the drivers of mental health during a pandemic. The team is working to develop AI techniques for social media data to understand emerging challenges that affect people during the pandemic and how these challenges impact mental health.

The project was approved under the CIFAR AI Catalyst Grants Program, which is intended to address the COVID-19 pandemic and catalyze new research areas and collaborations in machine learning, providing funding for innovative, high-risk, high-reward ideas and projects.

Over the first months of the pandemic, people have turned to social media in large numbers (Twitter recently reported that active users are up 23%). This expanding social media discourse provides a view into the experiences of people that is not available by other means, especially for marginalized groups, including people with limited access to health care, those with limited socioeconomic means, and undocumented immigrants. Researchers will use this wealth of social media data to understand mental health’s acute and long term drivers, as well as mitigating factors.

“AI has a key role to play in supporting and enabling the important work of public health experts,” says Dr. Fyshe, who is a professor at the University of Alberta, cross-appointed in the Departments of Computing Science and Psychology. “It’s crucial that we create integrated, multidisciplinary teams as we work to meet challenges presented by the COVID-19 pandemic. AI researchers have a lot to offer, but we should take our cues from epidemiologists and other healthcare specialists to ensure our solutions achieve maximum impact.”

The multidisciplinary project will leverage Dr. Fyshe’s expertise in Natural Language Processing.  She is joined by Dr. Chunara with expertise in machine learning, public health and social media analytics and Dr. Lizotte and trainee Brent Davis, who developed a social media analytics framework for public health practitioners.

Using data from Twitter and Reddit, the research team will extract time-varying latent linguistic factors – meaning changes in the topics people discuss over time – that track with either the rise of the pandemic or changes in established measures of mental health from text (Linguistic Inquiry and Word Count, for example). These factors will be associated with user groups that are defined by geotags, hashtags, or subreddit activity.

Interpretability of results will be paramount to support public health practitioners and policymakers as they develop and implement tailored mental health supports for different geographic and social groups. The team will create an online visual analytics system freely available to practitioners that will help them adapt to the changing experiences of the populations they serve. They will also maintain a blog to rapidly disseminate results and retrospectives.

The team will work with public health partners throughout the year-long project to ensure the resulting system aligns to the needs of practitioners and the public.

This research is based on work supported by the CIFAR AI and COVID Catalyst Grants. Learn more about Amii’s other projects under the CIFAR AI and Catalyst Grants.