Understanding Public Perception of COVID-19 Social Distancing on Twitter.

OBJECTIVE: Social distancing policies are key in curtailing COVID-19 infection spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We sought to study public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine learning models, conducting a sentiment analysis to identify emotions and polarity. We evaluated subjectivity of tweets and estimated frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half (50.4%) of tweets primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (i.e., joy), concerns about food insecurity and quarantine effects (i.e., fear), and unpredictability of COVID and its implications (i.e., surprise). CONCLUSIONS: The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing in the early stages of their implementation.