Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event
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DOI: 10.1371/journal.pone.0150190
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Cited by:
- Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
- Jiping Cao & Hartwig H. Hochmair & Fisal Basheeh, 2022. "The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users," Geographies, MDPI, vol. 2(3), pages 1-14, September.
- Rachel Samuels & John E. Taylor & Neda Mohammadi, 2020. "Silence of the Tweets: incorporating social media activity drop-offs into crisis detection," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1455-1477, August.
- Shi Shen & Ke Shi & Junwang Huang & Changxiu Cheng & Min Zhao, 2023. "Global online social response to a natural disaster and its influencing factors: a case study of Typhoon Haiyan," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
- Chenchen Yang & Han Zhang & Xunhua Li & Zongyi He & Junli Li, 2023. "Analysis of spatial and temporal characteristics of major natural disasters in China from 2008 to 2021 based on mining news database," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 1881-1916, September.
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