ArticlePloS one2017
Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 163 citations in OpenAlex.
- Understanding of income and race disparities in hurricane evacuation is contingent upon study case and design.Scientific reports · 2024Article
- Tracking electricity losses and their perceived causes using nighttime light and social media.iScience · 2023Article
- "Is a game really a reason for people to die?" Sentiment and thematic analysis of Twitter-based discourse on Indonesia soccer stampede.AIMS public health · 2023Article
- The Curious Case of Baby Formula in the United States in 2022: Cries for Urgent Action Months after Silence in the Midst of Alarm Bells.Food ethics · 2023Article
- Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data.Scientific reports · 2022Article
- The anatomy of natural disasters on online media: hurricanes and wildfires.Natural hazards (Dordrecht, Netherlands) · 2022Article
- Quantifying "local food" online and social media in the United States for 2018-2021.Agriculture & food security · 2022Article
- Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter.Scientific reports · 2021Article
- Measuring global multi-scale place connectivity using geotagged social media data.Scientific reports · 2021Article
- Human mobility in response to COVID-19 in France, Italy and UK.Scientific reports · 2021Article
- Hurricanes and hashtags: Characterizing online collective attention for natural disasters.PloS one · 2021Article
- Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.JMIR research protocols · 2020Article
- Compound Risks of Hurricane Evacuation Amid the COVID-19 Pandemic in the United States.GeoHealth · 2020Article
- Impact of extreme weather events on urban human flow: A perspective from location-based service data.Computers, environment and urban systems · 2020Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hurricane Matthew was the deadliest Atlantic storm since Katrina in 2005 and prompted one of the largest recent hurricane evacuations along the Southeastern coast of the United States. The storm and its projected landfall triggered a massive social media reaction. Using Twitter data, this paper examines the spatiotemporal variability in social media response and develops a novel approach to leverage geotagged tweets to assess the evacuation responses of residents. The approach involves the retrieval of tweets from the Twitter Stream, the creation and filtering of different datasets, and the statistical and spatial processing and treatment to extract, plot and map the results. As expected, peak Twitter response was reached during the pre-impact and preparedness phase, and decreased abruptly after the passage of the storm. A comparison between two time periods-pre-evacuation (October 2th-4th) and post-evacuation (October 7th-9th)-indicates that 54% of Twitter users moved away from the coast to a safer location, with observed differences by state on the timing of the evacuation. A specific sub-state analysis of South Carolina illustrated overall compliance with evacuation orders and detailed information on the timing of departure from the coast as well as the destination location. These findings advance the use of big data and citizen-as-sensor approaches for public safety issues, providing an effective and near real-time alternative for measuring compliance with evacuation orders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.