Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during Hurricane Laura compounded by the COVID-19 pandemic. Hurricane Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana, U.S. Using an application programming interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Hurricane Laura. Online social networks were based on Twitter’s user influence feature (i.e., mentions or tags) that allows notification of other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into 21 components of various size, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those which heavily involved and closely interacted with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters.
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Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions
Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.
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- NSF-PAR ID:
- 10318083
- Date Published:
- Journal Name:
- Journal of Emergency Management
- Volume:
- 19
- Issue:
- 7
- ISSN:
- 1543-5865
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5055/jem.0589
