  The COVID-19 pandemic has forced public health experts to develop contingent
policies to stem the spread of infection, including measures such as
partial/complete lockdowns. The effectiveness of these policies has varied with
geography, population distribution, and effectiveness in implementation.
Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than
others (e.g., United States) in curbing the outbreak. A data-driven
investigation into effective public health policies of a country would allow
public health experts in other nations to decide future courses of action to
control the outbreaks of disease and epidemics. We chose Spain and India to
present our analysis on regions that were similar in terms of certain factors:
(1) population density, (2) unemployment rate, (3) tourism, and (4) quality of
living. We posit that citizen ideology obtainable from twitter conversations
can provide insights into conformity to policy and suitably reflect on future
case predictions. A milestone when the curves show the number of new cases
diverging from each other is used to define a time period to extract
policy-related tweets while the concepts from a causality network of
policy-dependent sub-events are used to generate concept clouds. The number of
new cases is predicted using sentiment scores in a regression model. We see
that the new case predictions reflects twitter sentiment, meaningfully tied to
a trigger sub-event that enables policy-related findings for Spain and India to
be effectively compared.

Social and Information Networks;Physics and Society