Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

• Published in: Journal of medical Internet research Vol. 23; no. 2; p. e25431
• Main Authors: Jang, Hyeju, Rempel, Emily, Roth, David, Carenini, Giuseppe, Janjua, Naveed Zafar
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 10.02.2021; JMIR Publications
• Subjects: Analysis; Asian Continental Ancestry Group; Attitude to Health; Canada; Coronaviruses; COVID-19; Data mining; Disease Outbreaks; Disease transmission; Ebola virus; Epidemics; Experts; Health education; Humans; Intervention; Masks; Medical research; Misinformation; Natural Language Processing; North America; Original Paper; Pandemics; Public Health; Public interest; Racism; SARS-CoV-2; Sentiment analysis; Severe acute respiratory syndrome coronavirus 2; Social issues; Social Media; Social networks; Subject specialists; Topics; Tracking; United States; Vaccines; Canada; United States; North America; New York; California; Iran; United States > US; racism; reaction; sentiment analysis; anti-Asians; Twitter; topic modeling; North America; discourse; COVID-19; Canada; aspect-based sentiment analysis; social media; public health
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/25431

Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.