The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States

References

• ArcGIS Hub. (2022). USA Major Cities. Retrieved from https://hub.arcgis.com/datasets/esri::usa-major-cities/explore.
   Google Scholar
• Bakillah, M., R. Li, and S. Liang. 2015. “Geo-Located Community Detection in Twitter with Enhanced Fast-Greedy Optimization of Modularity: The Case Study of Typhoon Haiyan.” International Journal of Geographical Information Science 29 (2): 258–279. https://doi.org/10.1080/13658816.2014.964247.
   Web of Science ®Google Scholar
• Beigi, G., X. Hu, R. Maciejewski, and H. Liu. 2016. “An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief.” Studies in Computational Intelligence 639:313–340. https://doi.org/10.1007/978-3-319-30319-2_13.
   Google Scholar
• Cresswell, T. 2013. Geographic Thought: A Critical Introduction. Chichester, UK: Wiley-Blackwell.
   Google Scholar
• Encyclopaedia Britannica. (2021). Twitter: Microblogging Service. Accessed March 19, 2021, from https://www.britannica.com/topic/Twitter.
   Google Scholar
• Feng, Y., X. Huang, and M. Sester. 2022. “Extraction and Analysis of Natural Disaster-Related VGI from Social Media: Review, Opportunities and Challenges.” International Journal of Geographical Information Science 36 (7): 1275–1316. https://doi.org/10.1080/13658816.2022.2048835.
   Web of Science ®Google Scholar
• Gong, X., S. Peng, Y. Lu, S. Wang, X. Huang, and X. Ye. 2022. “Social Network Analysis of Nonprofits in Disaster Response: The Case of Twitter During the COVID-19 Pandemic in the United States.” Social Science Computer Review 1–26. https://doi.org/10.1177/08944393221130674.
   Web of Science ®Google Scholar
• Granell, C., and F. Ostermann. 2016. “Beyond Data Collection: Objectives and Methods of Research Using VGI and Geo-Social Media for Disaster Management.” Computers, Environment and Urban Systems 59:231–243. https://doi.org/10.1016/j.compenvurbsys.2016.01.006.
   Web of Science ®Google Scholar
• Han, S. Y., M. H. Tsou, and K. C. Clarke. 2018. “Revisiting the Death of Geography in the Era of Big Data: The Friction of Distance in Cyberspace and Real Space.” International Journal of Digital Earth 11 (5): 451–469. https://doi.org/10.1080/17538947.2017.1330366.
   Web of Science ®Google Scholar
• Haworth, B. 2016. “Emergency Management Perspectives on Volunteered Geographic Information: Opportunities, Challenges and Change.” Computers, Environment & Urban Systems 57:189–198. https://doi.org/10.1016/j.compenvurbsys.2016.02.009.
   Web of Science ®Google Scholar
• Haworth, B. 2018. “Implications of Volunteered Geographic Information for Disaster Management and GIScience: A More Complex World of Volunteered Geography.” Annals of the American Association of Geographers 108 (1): 226–240. https://doi.org/10.1080/24694452.2017.1321979.
   Web of Science ®Google Scholar
• Haworth, B., and E. Bruce. 2015. “A Review of Volunteered Geographic Information for Disaster Management.” Geography Compass 9 (5): 237–250. https://doi.org/10.1111/gec3.12213.
   Web of Science ®Google Scholar
• Houston, J. B., J. Hawthorne, M. F. Perreault, E. H. Park, M. G. Hode, M. R. Halliwell, S. E. Turner McGowen, et al. 2014. “Social Media and Disasters: A Functional Framework for Social Media Use in Disaster Planning, Response, and Research.” Disasters 39 (1): 1–22. https://doi.org/10.1111/disa.12092.
   PubMed Web of Science ®Google Scholar
• Huang, Q., and Y. Xiao. 2015. “Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery.” ISPRS International Journal of Geo-Information 4 (3): 1549–1568. https://doi.org/10.3390/ijgi4031549.
   Web of Science ®Google Scholar
• James, T. K., and H. Zagefka. 2017. “The Effects of Group Memberships of Victims and Perpetrators in Humanly Caused Disasters on Charitable Donations to Victims.” Journal of Applied Social Psychology 47 (8): 446–458. https://doi.org/10.1111/jasp.12452.
   Web of Science ®Google Scholar
• Jeong, H. J. 2020. “The Roles of Self-Identity Cues and Public Self-Consciousness in Volunteering for Socially Stigmatized Causes on Social Media.” Journal of Nonprofit & Public Sector Marketing 32 (1): 47–72. https://doi.org/10.1080/10495142.2019.1689221.
   Web of Science ®Google Scholar
• Joseph, J., K. Dev, A. P. Pradeepkumar, and M. Mohan. 2018. “Big Data Analytics and Social Media in Disaster Management.” Integrating Disaster Science and Management 287–294. https://doi.org/10.1016/B978-0-12-812056-9.00016-6.
   Google Scholar
• Landwehr, P., W. Wei, M. Kowalchuck, and K. Carley. 2016. “Using Tweets to Support Disaster Planning, Warning and Response.” Safety Science 90:33–47. https://doi.org/10.1016/j.ssci.2016.04.012.
   Web of Science ®Google Scholar
• Li, J., K. K. Stephens, Y. Zhu, and D. Murthy. 2019. “Using Social Media to Call for Help in Hurricane Harvey: Bonding Emotion, Culture, and Community Relationships.” International Journal of Disaster Risk Reduction 38 (June): 101212. https://doi.org/10.1016/j.ijdrr.2019.101212.
   Google Scholar
• Ludin, S. M., M. Rohaizat, and P. Arbon. 2019. “The Association Between Social Cohesion and Community Disaster Resilience: A Cross‐Sectional Study.” Health & Social Care in the Community 27 (3): 621–631. https://doi.org/10.1111/hsc.12674.
   PubMed Web of Science ®Google Scholar
• Martín, Y., S. L. Cutter, Z. Li, C. T. Emrich, and J. T. Mitchell. 2020. “Using Geotagged Tweets to Track Population Movements to and from Puerto Rico After Hurricane Maria.” Population and Environment 42 (1): 4–27. https://doi.org/10.1007/s11111-020-00338-6.
   Web of Science ®Google Scholar
• National Aeronautics and Space Administration. (2022a). Harvey Atlantic Ocean. Retrieved from https://www.nasa.gov/feature/goddard/2017/harvey-atlantic-ocean.
   Google Scholar
• National Aeronautics and Space Administration. (2022b). Laura Makes Landfall. Retrieved from https://earthobservatory.nasa.gov/images/147180/laura-makes-landfall.
   Google Scholar
• National Weather Service. (2022a). Major Hurricane Harvey August 25-29 2017. Retrieved from https://www.weather.gov/crp/hurricane_harvey.
   Google Scholar
• National Weather Service. (2022b). Hurricane Florence. September 14, 2018. Retrieved from https://www.weather.gov/ilm/HurricaneFlorence.
   Google Scholar
• Newkirk, R. T. 2001. “The Increasing Cost of Disasters in Developed Countries: A Challenge to Local Planning and Government.” Journal of Contingencies & Crisis Management 9 (3): 159–170. https://doi.org/10.1111/1468-5973.00165.
   Google Scholar
• Papadopoulos, S., Y. Kompatsiaris, A. Vakali, and P. Spyridonos. 2012. “Community Detection in Social Media.” Data Mining and Knowledge Discovery 24 (3): 515–554. https://doi.org/10.1007/s10618-011-0224-z.
   Web of Science ®Google Scholar
• Pew Research Center. (2019). Sizing Up Twitter Users. Retrieved from https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/.
   Google Scholar
• Phillips, B., N. David, and G. Webb. 2012. Introduction to Emergency Management. Boca Raton: Taylor & Francis Group.
   Google Scholar
• Pielke, R. A., J. Gratz, C. W. Landsea, D. Collins, M. A. Saunders, and R. Musulin. 2008. “Normalized Hurricane Damage in the United States: 1900–2005.” Natural Hazards Review 9 (1): 29–42. https://doi.org/10.1061/(ASCE)1527-6988(2008)9:1(29).
   Google Scholar
• Resch, B., F. Usländer, and C. Havas. 2018. “Combining Machine-Learning Topic Models and Spatiotemporal Analysis of Social Media Data for Disaster Footprint and Damage Assessment.” Cartography and Geographic Information Science 45 (4): 362–376. https://doi.org/10.1080/15230406.2017.1356242.
   Web of Science ®Google Scholar
• Sadri, A. M., S. Hasan, S. V. Ukkusuri, and M. Cebrian. 2018. “Crisis Communication Patterns in Social Media During Hurricane Sandy.” Transportation Research Record 2672 (1): 125–137. https://doi.org/10.1177/0361198118773896.
   Web of Science ®Google Scholar
• Sit, M. A., C. Koylu, and I. Demir. 2019. “Identifying Disaster-Related Tweets and Their Semantic, Spatial and Temporal Context Using Deep Learning, Natural Language Processing and Spatial Analysis: A Case Study of Hurricane Irma.” International Journal of Digital Earth 12 (11): 1205–1229. https://doi.org/10.1080/17538947.2018.1563219.
   Web of Science ®Google Scholar
• Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. A. Scaife. 2010. “Skillful Multi-Year Predictions of Atlantic Hurricane Frequency.” Nature Geoscience 3 (12): 846–849. https://doi.org/10.1038/ngeo1004.
   Web of Science ®Google Scholar
• Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46: 234. https://doi.org/10.2307/143141.
   Web of Science ®Google Scholar
• Tredoux, C. T., and K. Durheim. 2002. Numbers, Hypotheses and Conclusions: A Course in Statistics for the Social Sciences. Cape Town: UCT Press.
   Google Scholar
• Twitter. (2022). Academic Research Access. Retrieved from https://developer.twitter.com/en/products/twitter-api/academic-research.
   Google Scholar
• US Census Bureau. (2022a). 2018 Hurricane Florence. Retrieved from https://www.census.gov/topics/preparedness/events/hurricanes/florence.html.
   Google Scholar
• US Census Bureau. (2022b). 2020 Hurricane Laura. Retrieved from https://www.census.gov/topics/preparedness/events/hurricanes/laura.html.
   Google Scholar
• US Census Bureau. (2022c). Cartographic Boundary Files – Shapefile. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
   Google Scholar
• US Census Bureau. (2022d). TIGER/Line Shapefiles. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.
   Google Scholar
• Xu, J., and Y. Qiang. 2022. “Analysing Information Diffusion in Natural Hazards Using Retweets - a Case Study of 2018 Winter Storm Diego.” Annals of GIS 28 (2): 213–227. https://doi.org/10.1080/19475683.2021.1954086.
   Web of Science ®Google Scholar
• Yuan, F., M. Li, R. Liu, W. Zhai, and B. Qi. 2021. “Social Media for Enhanced Understanding of Disaster Resilience During Hurricane Florence.” International Journal of Information Management 57:102289. https://doi.org/10.1016/j.ijinfomgt.2020.102289.
   Web of Science ®Google Scholar
• Zhang, G. 2020. “Spatial and Temporal Patterns in Volunteer Data Contribution Activities: A Case Study of eBird.” ISPRS International Journal of Geo-Information 9 (10): 597. https://doi.org/10.3390/ijgi9100597.
   Web of Science ®Google Scholar
• Zhang, G. 2021. “Volunteered Geographic Information.” In Geographic Information Science & Technology Body of Knowledge, edited by J. P. Wilson. https://doi.org/10.22224/gistbok/2021.1.1.
   Google Scholar
• Zhang, G., and A. X. Zhu. 2018. “The Representativeness and Spatial Bias of Volunteered Geographic Information: A Review.” Annals of GIS 24 (3): 151–162. https://doi.org/10.1080/19475683.2018.1501607.
   Web of Science ®Google Scholar
• Zipf, G. K. 1949. Human Behavior and the Principle of Least Effort. Cambridge, MA: Addison Wesley Press.
   Google Scholar