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Online social networks allow different agencies and the public to interactand share the underlying risks and protective actions during major disasters.This study revealed such crisis communication patterns during hurricane Lauracompounded by the COVID-19 pandemic. Laura was one of the strongest (Category4) hurricanes on record to make landfall in Cameron, Louisiana. Using theApplication Programming Interface (API), this study utilizes large-scale socialmedia data obtained from Twitter through the recently released academic trackthat provides complete and unbiased observations. The data captured publiclyavailable tweets shared by active Twitter users from the vulnerable areasthreatened by Laura. Online social networks were based on user influencefeature ( mentions or tags) that allows notifying other users while posting atweet. Using network science theories and advanced community detectionalgorithms, the study split these networks into twenty-one components ofvarious sizes, 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 thesecommunities to observe their risk-taking or risk-averse behavior during a majorcompounding crisis. Social media accounts of local news media, radio,universities, and popular sports pages were among those who involved heavilyand interacted closely with local residents. In contrast, emergency managementand planning units in the area engaged less with the public. The findings ofthis study provide novel insights into the design of efficient social mediacommunication guidelines to respond better in future disasters.

Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19