Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions

TL;DR

This study analyzes how public agencies used social media during COVID-19 to communicate risks and influence community responses, employing data-driven methods to understand communication patterns and their impact on pandemic indicators.

Contribution

It provides a detailed analysis of agency communication strategies on social media during COVID-19 using machine learning techniques, revealing variations over time and across agencies.

Findings

Identified key topics discussed by agencies, such as face covering and social distancing.

Revealed differences in agency communication focus and timing.

Provided insights to improve future risk communication strategies.

Abstract

Risk and response communication of public agencies through social media played a significant role in the emergence and spread of novel Coronavirus (COVID-19) and such interactions were echoed in other information outlets. This study collected time-sensitive online social media data and analyzed such communication patterns from public health (WHO, CDC), emergency (FEMA), and transportation (FDOT) agencies using data-driven methods. The scope of the work includes a detailed understanding of how agencies communicate risk information through social media during a pandemic and influence community response (i.e. timing of lockdown, timing of reopening) and disease outbreak indicators (i.e. number of confirmed cases, number of deaths). The data includes Twitter interactions from different agencies (2.15K tweets per agency on average) and crowdsourced data (i.e. Worldometer) on COVID-19 cases and deaths were observed between February 21, 2020 and June 06, 2020. Several machine learning techniques such as (i.e. topic mining and sentiment ratings over time) are applied here to identify the dynamics of emergent topics during this unprecedented time. Temporal infographics of the results captured the agency-levels variations over time in circulating information about the importance of face covering, home quarantine, social distancing and contact tracing. In addition, agencies showed differences in their discussions about community transmission, lack of personal protective equipment, testing and medical supplies, use of tobacco, vaccine, mental health issues, hospitalization, hurricane season, airports, construction work among others. Findings could support more efficient transfer of risk and response information as communities shift to new normal as well as in future pandemics.