Improving social media use for disaster resilience: challenges and strategies

Figures & data

Figure 1. Overview of the framework.

Figure 2. Map of Harvey rescue request tweets in Houston, USA, showing widespread rescue requests in the impacted region.

Figure 3. County-level ratio indices during Hurricanes Sandy 2012 (left) and Harvey 2017 (right) showing high tweet ratios near the event (After Zou et al. 2019b).

Figure 4. Mean sentiment scores during Hurricanes Isaac, Sandy, and Harvey (After Wang K et al. 2021).

Table 1. Proposed strategies to improve Twitter use for disaster resilience (see text for detailed description).

Sector	Proposed Strategy
Organization (FR, GOV, NGO, NEWS)	1. Utilize the mitigation phase to build relationships and improve communications with community members  2. Raise awareness of official accounts, which serve as reliable sources for true information to reduce the chance of false news spread  3. Adopt social media use guidelines to facilitate faster responses to the questions and comments posted  4. Clearly define the mission regarding social media use and allocate resources needed to improve its use and management  5. Upgrade the wired 911 system to enable connection with web-based social media rescue requests  6. Design a template to allow speedy and accurate rescue request and report the rescue status  7. Develop a GIS for mapping rescue requests and planning
Individual	8. Would like to have a mechanism to check the validity of the news and a warning of spreading the false news  9. Include hashtags in messaging to help organize response and improve communication 10. Would like to know a list of relevant hashtags before the disaster event that would accept rescue requests 11. Would like to know if rescue request tweets reach the addressee and when help will come
Social media company	12. Make the platform easier for developing templates for emergency use 13. Work with emergency-related organizations to ensure the changes are beneficial 14. Develop methods to alert potential false information and warning for spreading it 15. Help setting a central website to debunk false information 16. Work with the social media communities to explore ways to increase locational accuracy of the tweets while protecting privacy
Researcher	17. Develop a cyberinfrastructure with data and tools 18. Develop methods to handle Twitter data bias 19. Develop methods to reduce the potential bias in AI or non-AI based algorithms 20. Develop methods to detect false news

Figure 5. An example of Twitter emergency rescue request template (Zou et al. 2023).

Zou, Lei, Nina S. N. Lam, Shayan Shams, Heng Cai, Michelle A. Meyer, Seungwon Yang, Kisung Lee, Seung-Jong Park, and Margaret A. Reams. 2019b. “Social and Geographical Disparities in Twitter Use During Hurricane Harvey.” International Journal of Digital Earth 12 (11): 1300–1318. https://doi.org/10.1080/17538947.2018.1545878.

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Zou, Lei, Danqing Liao, Nina S. N. Lam, Michelle A. Meyer, Nasir G. Gharaibeh, Heng Cai, Bing Zhou, and Dongying Li. 2023. “Social Media for Emergency Rescue: An Analysis of Rescue Requests on Twitter During Hurricane Harvey.” International Journal of Disaster Risk Reduction 85: 103513. https://doi.org/10.1016/j.ijdrr.2022.103513.

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Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.