Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-01T03:06:54.725Z Has data issue: false hasContentIssue false
  • English
  • Français

Exploring Social Media Network Connections to Assist During Public Health Emergency Response: A Retrospective Case-Study of Hurricane Matthew and Twitter Users in Georgia, USA

Published online by Cambridge University Press:  17 February 2023

Kamalich Muniz-Rodriguez Open the ORCID record for Kamalich Muniz-Rodriguez [Opens in a new window] ,
Jessica S. Schwind Open the ORCID record for Jessica S. Schwind [Opens in a new window] ,
Jingjing Yin ,
Hai Liang ,
Gerardo Chowell  and
Isaac Chun-Hai Fung Open the ORCID record for Isaac Chun-Hai Fung [Opens in a new window]
Kamalich Muniz-Rodriguez*
Affiliation:
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA Ponce Research Institute, Ponce Medical School Foundation, Ponce, Puerto Rico
Jessica S. Schwind
Affiliation:
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
Jingjing Yin
Affiliation:
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
Hai Liang
Affiliation:
School of Journalism and Communication, The Chinese University of Hong Kong, Hong Kong
Gerardo Chowell
Affiliation:
Department of Population Health Sciences, Georgia State University, Atlanta, GA, USA
Isaac Chun-Hai Fung
Affiliation:
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
*
Corresponding author: Kamalich Muniz-Rodriguez, Email: km11200@georgiasouthern.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

To assist communities who suffered from hurricane-inflicted damages, emergency responders may monitor social media messages. We present a case-study using the event of Hurricane Matthew to analyze the results of an imputation method for the location of Twitter users who follow school and school districts in Georgia, USA.

Methods:

Tweets related to Hurricane Matthew were analyzed by content analysis with latent Dirichlet allocation models and sentiment analysis to identify needs and sentiment changes over time. A hurdle regression model was applied to study the association between retweet frequency and content analysis topics.

Results:

Users residing in counties affected by Hurricane Matthew posted tweets related to preparedness (n = 171; 16%), awareness (n = 407; 38%), call-for-action or help (n = 206; 19%), and evacuations (n = 93; 9%), with mostly a negative sentiment during the preparedness and response phase. Tweets posted in the hurricane path during the preparedness and response phase were less likely to be retweeted than those outside the path (adjusted odds ratio: 0.95; 95% confidence interval: 0.75, 1.19).

Conclusions:

Social media data can be used to detect and evaluate damages of communities affected by natural disasters and identify users’ needs in at-risk areas before the event takes place to aid during the preparedness phases.

Keywords

Twittersocial mediacontent analysisnatural disasterslocation
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e315
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Finch, KC, Snook, KR, Duke, CH, et al. Public health implications of social media use during natural disasters, environmental disasters, and other environmental concerns. Nat Hazards (Dordr). August 01 2016;83(1):729-760. doi: 10.1007/s11069-016-2327-8 CrossRefGoogle Scholar
Kabir, AI, Karim, R, Newaz, S, et al. The power of social media analytics: text analytics based on sentiment analysis and word clouds on R. Informatica Economica. 2018;22(1):25-38. doi: 10.12948/issn14531305/22.1.2018.03 CrossRefGoogle Scholar
Sherchan, W, Pervin, S, Butler, CJ, et al. Harnessing Twitter and Instagram for disaster management. IBM J Res Dev. 2017. doi: 10.1147/JRD.2017.2729238 CrossRefGoogle Scholar
Fu, K-w, White, J, Chan, Y-y, et al. Enabling the disabled: media use and communication needs of people with disabilities during and after the Sichuan earthquake in China. Int J Emerg Manag. 2010/01/01 2010;7(1):75-87. doi: 10.1504/IJEM.2010.032046 CrossRefGoogle Scholar
Muniz-Rodriguez, K, Ofori, SK, Bayliss, LC, et al. Social media use in emergency response to natural disasters: a systematic review with a public health perspective. Disaster Med Public Health Prep. 2020;14(1):139-149. doi: 10.1017/dmp.2020.3 Google ScholarPubMed
Kim, J, Hastak, M. Social network analysis: characteristics of online social networks after a disaster. Int J Inform Manag. 2018;38(1):86-96. doi: 10.1016/j.ijinfomgt.2017.08.003 CrossRefGoogle Scholar
Adams, J, Raeside, R, Khan, HTA. Research Methods for Business and Social Science Students. 2nd edition. Sage Publications Pvt. Ltd; 2014.Google Scholar
Kiatpanont, R, Tanlamai, U, Chongstitvatana, P. Extraction of actionable information from crowdsourced disaster data. J Emerg Manag. 2016 2016;14(6):377-390. doi: 10.5055/jem.2016.0302 CrossRefGoogle ScholarPubMed
Andrews, S, Gibson, H, Domdouzis, K, et al. Creating corroborated crisis reports from social media data through formal concept analysis. J Intell Inf Syst. 2016;47(2):287-312. doi: 10.1007/s10844-016-0404-9 Google Scholar
Brandt, HM, Turner-McGrievy, G, Friedman, DB, et al. Examining the role of Twitter in response and recovery during and after historic flooding in South Carolina. J Public Health Manag Pract. 2019;25(5):E6-E12. doi: 10.1097/phh.0000000000000841 CrossRefGoogle ScholarPubMed
Cervone, G, Sava, E, Huang, Q, et al. Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. Int J Remote Sens. 2016;37(1):100-124. doi: 10.1080/01431161.2015.1117684 CrossRefGoogle Scholar
Kaufhold, M-A, Reuter, C. The self-organization of digital volunteers across social media: the case of the 2013 European floods in Germany. J Homel Secur Emerg Manag. 2016;13(1):137-166. doi: 10.1515/jhsem-2015-0063 Google Scholar
Tang, Z, Zhang, L, Xu, F, et al. Examining the role of social media in California’s drought risk management in 2014. Nat Hazards. Oct 2015;79(1):171-193. doi: 10.1007/s11069-015-1835-2 Google Scholar
Muniz-Rodriguez, K. Social Media Data Analysis, a Tool for Public Health Emergency Management During Natural Disasters. Electronic Theses and Dissertations. Georgia Southern University; 2020. https://digitalcommons.georgiasouthern.edu/etd/2175 Google Scholar
National Hurricane Center. 2016 Atlantic Hurricane Season. National Oceanic and Atmospheric Administration. Accessed August 3, 2020. https://www.nhc.noaa.gov/data/tcr/index.php?season=2016&basin=atl Google Scholar
National Hurricane Center. 2017 Atlantic Hurricane Season. National Oceanic and Atmospheric Administration. Accessed August 3, 2020. https://www.nhc.noaa.gov/data/tcr/index.php?season=2017&basin=atl Google Scholar
National Hurricane Center. Glossary of NHC Terms. National Oceanic and Atmospheric Administration. Accessed October 11, 2020. https://www.nhc.noaa.gov/aboutgloss.shtml Google Scholar
Ahweyevu, JO, Chukwudebe, NP, Buchanan, BM, et al. Using Twitter to track unplanned school closures: Georgia public schools, 2015-17. Disaster Med Public Health Prep. 2021;15(5):568-572. doi: 10.1017/dmp.2020.65 CrossRefGoogle ScholarPubMed
United States Census Bureau. About. Updated October 15, 2018. Accessed October 25, 2019. https://www.census.gov/programs-surveys/metro-micro/about.html Google Scholar
Grün, B, Hornik, K. topicmodels: An R package for fitting topic models. Cran.R-Project. Accessed July 15, 2020. https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf Google Scholar
Blei, DM. Probabilistic topic models. Commun ACM. 2012;55(4):77-84. doi: 10.1145/2133806.2133826 Google Scholar
Ghatak, A. Machine Learning with R. Springer; 2017:224.Google Scholar
Kumar, A. Mastering Text Mining with R. 1st ed. Packt Publishing Ltd; 2016.Google Scholar
Adnan, MM, Yin, J, Jackson, AM, et al. World Pneumonia Day 2011-2016: Twitter contents and retweets. Int Health. 2019;11(4):297-305. doi: 10.1093/inthealth/ihy087 CrossRefGoogle ScholarPubMed
Silge, J, Robinson, D. Text Mining with R: A Tidy Approach. O’Reilly Media; 2019. https://www.tidytextmining.com Google Scholar
United States Census Bureau. Country profile: Georgia. Accessed August 20, 2020. https://data.census.gov/cedsci/table?q=United%20States&g=0400000US13_310M200US10500,12060,15260,25980_310M300US12020,17980,19140,23580,25980,31420,40660,42340,46660,47580&y=2016&tid=ACSST1Y2016.S0101&hidePreview=false Google Scholar
Love, TE. Data science for biological, medical and health research: notes for 432. Updated May 1, 2018. Accessed September 5, 2020. https://thomaselove.github.io/432-notes/index.html Google Scholar
Rodriguez, G. Mean and variance in models for count data. Princeton University. Accessed September 13, 2020. https://data.princeton.edu/wws509/notes/countmoments Google Scholar
National Hurricane Center. Hurricane Matthew (AL142016). Updated April 7, 2017. Accessed September 1, 2020, 2020. https://www.nhc.noaa.gov/data/tcr/AL142016_Matthew.pdf Google Scholar
Federal Emergency Management Agency. The four phases of emergency management Accessed December 11, 2018. https://training.fema.gov/emiweb/downloads/is10_unit3.doc Google Scholar
Federal Emergency Management Agency. Georgia Hurricane Matthew (DR-4284-GA). FEMA. Updated March 20, 2020. Accessed August 31, 2020. https://www.fema.gov/disaster/4284 Google Scholar
Liang, H, Shen, F, Fu K-w. Privacy protection and self-disclosure across societies: a study of global Twitter users. New Media Society. 2016;19(9):1476-1497. doi: 10.1177/1461444816642210 Google Scholar
Grasso, V, Crisci, A. Codified hashtags for weather warning on Twitter: an Italian case study. PLoS Curr. 2016. doi: 10.1371/currents.dis.967e71514ecb92402eca3bdc9b789529 CrossRefGoogle Scholar
Yuan, F, Liu, R. Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study. Int J Disaster Risk Reduct. 2018;28:758-767. doi: 10.1016/j.ijdrr.2018.02.003 CrossRefGoogle Scholar
Kryvasheyeu, Y, Chen, H, Obradovich, N, et al. Rapid assessment of disaster damage using social media activity. Sci Adv. 2016;2(3):e1500779. doi: 10.1126/sciadv.1500779 CrossRefGoogle ScholarPubMed
Zou, L, Lam, NSN, Cai, H, et al. Mining Twitter data for improved understanding of disaster resilience. Ann Am Assoc Geographers. 2018;108(5):1422-1441. doi: 10.1080/24694452.2017.1421897 Google Scholar
David, CC, Ong, JC, Legara, EF. Tweeting Supertyphoon Haiyan: evolving functions of Twitter during and after a disaster event. PLoS One. 2016;11(3):e0150190. doi: 10.1371/journal.pone.0150190 CrossRefGoogle ScholarPubMed
Kim, J, Bae, J, Hastak, M. Emergency information diffusion on online social media during storm Cindy in US. Int J Inf Manag. 2018;40:153-165. doi: 10.1016/j.ijinfomgt.2018.02.003 CrossRefGoogle Scholar
Liang, H, Fung, IC, Tse, ZTH, et al. How did Ebola information spread on twitter: broadcasting or viral spreading? BMC Public Health. 2019/04/25 2019;19(1):438. doi: 10.1186/s12889-019-6747-8 Google ScholarPubMed
Comunello, F, Parisi, L, Lauciani, V, et al. Tweeting after an earthquake: user localization and communication patterns during the 2012 Emilia seismic sequence. Ann Geophys. 2016;59(5). doi: 10.4401/ag-6945 CrossRefGoogle Scholar
Zahra, K, Ostermann, FO, Purves, RS. Geographic variability of Twitter usage characteristics during disaster events. Geo Spat Inf Sci. 2017;20(3):231-240. doi: 10.1080/10095020.2017.1371903 CrossRefGoogle Scholar
Jackson, AM, Mullican, LA, Tse, ZTH, et al. Unplanned closure of public schools in Michigan, 2015-2016: cross-sectional study on rurality and digital data harvesting. J Sch Health. 2020;90(7):511-519.Google ScholarPubMed
Supplementary material: File

Muniz-Rodriguez et al. supplementary material

Muniz-Rodriguez et al. supplementary material

Download Muniz-Rodriguez et al. supplementary material(File)
File 3.6 MB