The document discusses using social media analysis to understand how people respond to COVID-19 lockdowns and inform models of disease spread. It analyzes Twitter data from New York City during the initial lockdown period to assess public sentiment towards quarantine restrictions. Preliminary results found that most tweets expressed acceptance of quarantine, but pushback may increase over time. Social media analysis could provide insight into how compliance with lockdowns and social distancing affects disease transmission parameters in models.

1. How People Respond to COVID-19 Lockdown
a social media analysis

2. Spread of the Coronavirus in the U.S.
• Is travel history of NY is a key factor?
• A dataset of travel track based on location pings from the phones

3. Spread of the Coronavirus in the U.S.
• Yes! Travel history of NY is a key factor

4. Next?
Can we learn more from similar data
analysis than a well developed model
of infectious disease (e.g., Suspectable,
Infectious, Recovered (SIR) model)?
Probably not!

5. What could be the unique
contribution of data science?
Parameterization!
• In the SIR model, the model behavior is very sensitive to
certain parameters. For example, those related to lockdown.
• How effective the measures like lockdown or social
distancing are highly depends on people’s compliance.
• Social media analysis may provide a realistic understanding
of the ‘effectiveness’ of these strict measures, to better
inform model parameterization

6. Preliminary results
• 387 twitter: “quarantine” from NYC between Mar 13 and Apr12
• The results are based on my own judgement
• Most people are ok with quarantine, but we may see more pushback

7. • The ‘textblob’ package assigns a sentiment value to each twitter
based on the average sentiment value of each individual word
• Algorithm returns fairly different results