Despite the increasingly prominent role of social media in disaster events, studies analyzing its use in rescue operations remain scanty. Hurricane Harvey hit Texas with unprecedented rainfall and flooding in 2017 and was marked by widespread use of social media for rescue requests. We conducted a survey of 195 Twitter users in Houston and surrounding communities who had requested for rescue during Harvey. The objective was to investigate our targeted group's socioeconomic and flood exposure characteristics, report the effectiveness of Twitter, and highlight lessons learnt and suggestions made for its use in future rescue missions. Survey revealed that those requesting rescue on Twitter were better educated, employed (80%), and homeowners (81%). Majority of them were flooded (87%), but remained satisfied with current location and did not consider moving. Calling relatives and friends for rescue was most responsive and yielded higher assistance-provided rate than using Twitter. Our respondents found Twitter helpful, but identified issues such as not knowing when volunteers received their requests or whether they would send help. They suggested promoting Twitter accounts and hashtags that accept emergency requests. This study provides baseline information and actionable suggestions for first responders, community managers, and resilience practitioners to improve future rescue missions.

Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilience? Specifically, will communities with more disaster-related Twitter uses be more resilient to disasters, presumably because they have better situational awareness? The underlying issue is that if there are social and geographical disparities in Twitter use, how will such disparities affect communities’ resilience to disasters? This study examines the relationship between Twitter use and community resilience during Hurricane Isaac, which hit Louisiana and Mississippi in August 2012. First, we applied the resilience inference measurement (RIM) model to calculate the resilience indices of 146 affected counties. Second, we analyzed Twitter use and their sentiment patterns through the three phases of Hurricane Isaac—preparedness, response, and recovery. Third, we correlated Twitter use density and sentiment scores with the resilience scores and major social–environmental variables to test whether significant geographical and social disparities in Twitter use existed through the three phases of disaster management. Significant positive correlations were found between Twitter use density and resilience indicators, confirming that communities with higher resilience capacity, which are characterized by better social–environmental conditions, tend to have higher Twitter use. These results imply that Twitter use during disasters could be improved to increase the resilience of affected communities. On the other hand, no significant correlations were found between sentiment scores and resilience indicators, suggesting that further research on sentiment analysis may be needed.

Disaster resilience describes the ability of a community to bounce back from disaster impacts by resilience building activities. Social media provides an innovative way to observe human attitudes and responses, especially during disasters. However, most previous social media and disasters studies were conducted at a coarse spatial scale such as by county. This study analyzes Twitter activities during Hurricane Sandy in 2012, at the county and the zip code area levels in the five affected states. The study examines two questions: (1) will the relationships between disparities in social media use and disparities in disaster resilience found at the county level in previous studies still hold at the zip code area level? And (2) what new information or patterns can be revealed with the zip code area level analysis? Results show that correlations between Twitter use indices and social-environmental variables representing community resilience found at the county level in previous studies still hold, but they are weaker at the zip code area level. The study also shows that zip code areas that have major transportation hubs and commercial activities or low night-time population are major factors affecting Twitter use indices and hence the correlations. Future research should consider adding data on land use types and population dynamics to help improve social media use for disaster resilience analysis. Furthermore, employing a multiscale analysis approach can reduce uncertainties involved in analysis and obtain a more thorough understanding of the relationships between Twitter use and geographical and socioeconomic characteristics of the affected communities.

The abundance of available information on social media can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter- and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users in Washington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets in Washington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’ messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing.