Disaster Recovery Communication in the Digital Era: Social Media and the 2016 Southern Louisiana Flood

Local emergency management's use of social media during disasters: a case study of Hurricane Irma

Governments' use of social media during all phases of emergency management, especially during disasters, has increased dramatically in the past 20 years. Yet, implementation at the local government level in the United States remains haphazard. As technology and the role of social media evolve, there persists a need to understand the socio-technical aspects of social media's employment in times of disaster. This study contributes to the growing social media literature by asking the following questions: what challenges remain and what lessons learned are being institutionalised at the local level of government? A qualitative analysis of 26 after action reports on Hurricane Irma (September 2017) by county, state, and federal governments and a four-hour focus-group session revealed dominant and subdominant themes, including: push/pull information; capacity and technical issues; inconsistent messaging; one-way versus two-way communication; timing of messages; and data collection. The paper concludes by discussing lessons learned, remaining challenges, evidence of organisational learning, and recommendations for future research.

Dynamics of interorganisational emergency communication on Twitter: the case of Hurricane Irma

This study examines how county-level emergency management offices (EMOs) used Twitter to communicate with other public agencies and non-profit and for-profit organisations before, during, and after Hurricane Irma in 2017. It assesses the strategies that EMOs and other stakeholders employed to communicate risks on Twitter, concluding that its potential has not been fully exploited. EMOs only frequently interacted with a few non-profit and for-profit organisations, despite their involvement in emergency communication. While EMOs and other public agencies emphasised information dissemination and called on citizens to act and be prepared for the hurricane, non-profits tended to stress service and resource-related information, encouraged others to assist with disaster response, and provided emotional support. For-profits, meanwhile, actively addressed customers' concerns through direct two-way communication. Our findings indicate that EMOs should integrate non-profit and for-profit organisations' communication efforts, engaging them in important conversations on Twitter and advocating the use of highly relevant hashtags at different disaster management stages.

Protective consumption behavior under smog: using a data-driven dynamic Bayesian network

In the midst of the deteriorating air pollution and collective stress, people pay close attention to risk mitigation measures such as keeping indoor and purchasing anti-smog products. Through impact evaluations, factors regarding health protective behavior can be identified. However, limited research is available regarding probabilistic interdependencies between the factors and protective behavior and largely relies on subjective diagnosis. These concerns have led us to adopt a data-driven static Bayesian network (BN) and Dynamic BN model to help explore multidimensional factors that may influence the public's health protective behavior of buying anti-smog air purifiers and examine the dependencies among network nodes. Using the city-level aggregate data from an online shopping platform, the results shed new light on relationships existing among 11 factors and protective behavior of buying air purifiers. Furthermore, taking into account the dynamic nature of protective behavior, we add time-related factors on the basis of static BN to construct the dynamic BN model. Results indicate that PM2.5 concentration and product price are the two leading factors affecting the consumption behavior for air purifiers. Additionally, media-related factors play an important role in the consumption behavior. This study contributes to the fields of impact evaluation of protective consumption behavior analysis and links environment risk with public consumption by identifying key factors.

Twitter data from the 2019-20 Australian bushfires reveals participatory and temporal variations in social media use for disaster recovery

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019-20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort.

Use of Big Data in Disaster Recovery: An Integrative Literature Review

OBJECTIVE

Disasters of all varieties have been steadily increasing in frequency. Simultaneously, "big data" has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review.

METHODS

Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature.

RESULTS

After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health.

CONCLUSIONS

Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.