Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-23T05:56:58.104Z Has data issue: false hasContentIssue false

Challenges to Transforming Unconventional Social Media Data into Actionable Knowledge for Public Health Systems During Disasters

Published online by Cambridge University Press:  15 October 2019

Jennifer L. Chan*
Affiliation:
Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
Hemant Purohit
Affiliation:
Department of Information Sciences and Technology, Volgenau School of Engineering, George Mason University, Fairfax, Virginia
*
Correspondence and reprint requests to Jennifer L. Chan 211 E. Ontario Street, Suite 200, Chicago, IL 60611 (e-mail: [email protected]).

Abstract

Every year, there are larger and more severe disasters and health organizations are struggling to respond with services to keep public health systems running. Making decisions with limited health information can negatively affect response activities and impact morbidity and mortality. An overarching challenge is getting the right health information to the right health service personnel at the right time. As responding agencies engage in social media (eg, Twitter, Facebook) to communicate with the public, new opportunities emerge to leverage this non-traditional information for improved situational awareness. Transforming these big data is dependent on computers to process and filter content for health information categories relevant to health responders. To enable a more health-focused approach to social media analysis during disasters, 2 major research challenges should be addressed: (1) advancing methodologies to extract relevant information for health services and creating dynamic knowledge bases that address both the global and US disaster contexts, and (2) expanding social media research for disaster informatics to focus on health response activities. There is a lack of attention on health-focused social media research beyond epidemiologic surveillance. Future research will require approaches that address challenges of domain-aware, including multilingual language understanding in artificial intelligence for disaster health information extraction. New research will need to focus on the primary goal of health providers, whose priority is to get the right health information to the right medical and public health service personnel at the right time.

Type
Concepts in Disaster Medicine
Copyright
Copyright © 2019 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

REFERENCES

Centre for Research on the Epidemiology of Disasters (CRED). Natural disasters 2017. Brussels, Belgium: CRED; 2017.Google Scholar
United Nations Office for the Coordination of Humanitarian Affairs (OCHA). World humanitarian data and trends 2016. Published 2016. http://interactive.unocha.org/publication/2016_datatrends/. Accessed May 29, 2017.Google Scholar
The White House. The federal response to Hurricane Katrina: lessons learned; chapter five: lessons learned. Published 2005. https://georgewbush-whitehouse.archives.gov/reports/katrina-lessons-learned/chapter5.html. Accessed December 27, 2018.Google Scholar
Redlener, I, Reilly, MJ. Lessons from Sandy – preparing health systems for future disasters. N Engl J Med. 2012;367(24):22692271. doi:10.1056/NEJMp1213844.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention (CDC). Community Assessment for Public Health Emergency Response (CASPER) Toolkit. Published 2012. https://www.cdc.gov/nceh/hsb/disaster/CASPER_elearning/CASPERToolkit Version 2 0_FINAL CLEARED.pdf. Accessed December 27, 2018.Google Scholar
The Sphere Handbook. Humanitarian charter and minimum standards in humanitarian response. Published 2018. https://handbook.spherestandards.org/. Accessed December 13, 2018.Google Scholar
Agency for Healthcare and Research Quality. Defining health systems. 2016. https://www.ahrq.gov/chsp/chsp-reports/resources-for-understanding-health-systems/defining-health-systems.html. Accessed September 30, 2019.Google Scholar
Centers for Disease Control and Prevention. The public health system and the 10 essential public health services. 2018. https://www.cdc.gov/publichealthgateway/publichealthservices/essentialhealthservices.ml. Accessed September 30, 2019.Google Scholar
Goyet, CDV De, Sarmiento, JP, Grünewald, F. Health response to the earthquake in Haiti. 2011 Google Scholar
Landry, MD, O’Connell, C, Tardif, G, Burns, A. Post-earthquake Haiti: the critical role for rehabilitation services following a humanitarian crisis. Disabil Rehabil. 2010;32(19):16161618. doi:10.3109/09638288.2010.500345.CrossRefGoogle ScholarPubMed
Waugaman, A, Fast, L. Fighting Ebola with information: learning from data and information flows in the West Africa Ebola response. Published 2016. https://www.usaid.gov/sites/default/files/documents/15396/FightingEbolaWithInformation.pdf. Accessed April 2, 2018.Google Scholar
Centers for Disease Control and Prevention. Disaster information for people with chronic conditions and disabilities/natural disasters and severe weather. Published 2018. https://www.cdc.gov/disasters/chronic.html. Accessed December 27, 2018.Google Scholar
Rabkin, M, Fouad, FM, El-Sadr, WM. Addressing chronic diseases in protracted emergencies: lessons from HIV for a new health imperative. Glob Public Health. 2018;13(2):227233. doi:10.1080/17441692.2016.1176226.CrossRefGoogle ScholarPubMed
Collins, SP, Pang, PS, Lindsell, CJ, et al. International variations in the clinical, diagnostic, and treatment characteristics of emergency department patients with acute heart failure syndromes. Eur J Hear Fail. 2010;12(11):12531260. doi:10.1093/eurjhf/hfq133.CrossRefGoogle ScholarPubMed
Arrieta, MI, Foreman, RD, Crook, ED, Icenogle, ML. Providing continuity of care for chronic diseases in the aftermath of Katrina: from field experience to policy recommendations. Disaster Med Public Health Prep. 2009;3(03):174182. doi:10.1097/DMP.0b013e3181b66ae4.CrossRefGoogle ScholarPubMed
Sharma, AJ, Weiss, EC, Young, SL, et al. Chronic disease and related conditions at emergency treatment facilities in the New Orleans area after Hurricane Katrina. Disaster Med Public Health Prep. 2008;2(01):2732. doi:10.1097/DMP.0b013e31816452f0.CrossRefGoogle ScholarPubMed
Powell, T, Hanfling, D, Gostin, LO. Emergency preparedness and public health. JAMA. 2012;308(24):2569. doi:10.1001/jama.2012.108940.CrossRefGoogle ScholarPubMed
Bennett, J, Bertrand, W, Harkin, C, et al. Coordination of international humanitarian assistance in tsunami-affected countries. Published July 2006. https://www.sida.se/contentassets/4a691a64e0f9430f8abcf55c3250dc99/coordination-of-international-humanitarian-assistance-in-tsunami-affected-countries_3143.pdf. Accessed February 28, 2018.Google Scholar
Tsunami Evaluation Coalition. The role of needs assessment in the tsunami response London. Published 2006. https://www.alnap.org/system/files/content/resource/files/main/needs-assessment-final-report.pdf. Accessed March 1, 2018.Google Scholar
Centers for Disease Control and Prevention (CDC). Deaths associated with Hurricane Sandy. Published October–November 2012. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6220a1.htm. Accessed December 27, 2018.Google Scholar
Rabin, C. Death toll caused by Hurricane Irma now at 75. Miami Herald. Published 2017. https://www.miamiherald.com/news/weather/hurricane/article175029276.html. Accessed December 27, 2018.Google Scholar
Dosa, DM, Hyer, K, Brown, LM, et al. The controversy inherent in managing frail nursing home residents during complex hurricane emergencies. J Am Med Dir Assoc. 2008;9(8):599604. doi:10.1016/j.jamda.2008.05.007.CrossRefGoogle ScholarPubMed
Wright, P. At Tampa Hospital in evacuation zone, 800 patients and staff ride out Hurricane Irma. The Weather Channel. Published 2017. https://weather.com/storms/hurricane/news/hurricane-irma-tampa-hospital-evacuation-zone. Accessed December 27, 2018.Google Scholar
Hullah, E, Llewellyn, P, Aryal, KR, et al. Nepal Gorkha earthquake – internal real time evaluation on emergency health response service of Nepal. Published 2015. https://www.nrcs.org/sites/default/files/resources/1.Real Time Evaluation of Emergency Health Response Service of Nepal.pdf. Accessed December 30, 2018.Google Scholar
Evaluation of UNICEF’s response to the Ebola outbreak in evaluation office. Published 2017. https://reliefweb.int/sites/reliefweb.int/files/resources/2232-UNICEF-Ebola_Eval_report_web.pdf. Accessed December 30, 2018.Google Scholar
Gates, B. The next epidemic – lessons from Ebola. N Engl J Med. 2015;372(15):13811384. doi:10.1056/NEJMp1502918.CrossRefGoogle ScholarPubMed
Grundy, J, Biggs, B-A, Hipgrave, DB. Public health and international partnerships in the democratic people’s Republic of Korea. PLoS Med. 2015;12(12):e1001929. doi:10.1371/journal.pmed.1001929.Google ScholarPubMed
Tapp, C, Burkle, FM, Wilson, K, et al. Iraq War mortality estimates: a systematic review. Confl Health. 2008;2(1):1. doi:10.1186/1752-1505-2-1.CrossRefGoogle ScholarPubMed
Roberts, L, Lafta, R, Garfield, R, et al. Mortality before and after the 2003 invasion of Iraq: cluster sample survey. Lancet. 2004;364(9448):18571864. doi:10.1016/S0140-6736(04)17441-2.CrossRefGoogle ScholarPubMed
Burnham, G, Lafta, R, Doocy, S, Roberts, L. Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey. Lancet. 2006;368(9545):14211428. doi:10.1016/S0140-6736(06)69491-9.CrossRefGoogle ScholarPubMed
Kishore, N, Marqués, D, Mahmud, A, et al. Mortality in Puerto Rico after Hurricane Maria. N Engl J Med. 2018;379(2):162170.CrossRefGoogle ScholarPubMed
Santos-Burgoa, C, Sandberg, J, Suárez, E, et al. Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis. Lancet Planetary Health. 2018;2(11):e478e488.CrossRefGoogle ScholarPubMed
Robles, F, Kenan, D, Fink, S, Almukhtar, S. Official toll in Puerto Rico: 64. Actual deaths may be 1,052. New York Times. December 2017. https://www.nytimes.com/interactive/2017/12/08/us/puerto-rico-hurricane-maria-death-toll.html. Accessed September 30, 2019.Google Scholar
Crowley, J, Chan, J. Disaster Relief 2.0: The future of information sharing in humanitarian emergencies. Published 2011. https://hhi.harvard.edu/publications/disaster-relief-20-future-information-sharing-humanitarian-emergencies. Accessed September 30, 2019.Google Scholar
Altay, N, Labonte, M. Challenges in humanitarian information management and exchange: evidence from Haiti. Disasters. 2014;38(suppl 1):5072.10.1111/disa.12052CrossRefGoogle ScholarPubMed
Haiti earthquake: breaking new ground in the humanitarian information landscape. Washington DC: US Department of State, Humanitarian Information Unit; 2010.Google Scholar
Purohit, H, Castillo, C, Diaz, F, et al. Emergency-relief coordination on social media: automatically matching resource requests and offers. First Monday. 2013;19(1). doi:10.5210/fm.v19i1.4848.CrossRefGoogle Scholar
Van de Walle, B, Brugghemans, B, Comes, T. Improving situation awareness in crisis response teams: an experimental analysis of enriched information and centralized coordination. Int J Hum Comput Stud. 2016;95:6679. doi:10.1016/J.IJHCS.2016.05.001.CrossRefGoogle Scholar
@MarlaABC13. #Houston Med Center underwater, septic issue causing flooding in Ben Taub basement. Affecting food and supplies. https://twitter.com/MarlaABC13/status/901838658126393344. Posted 27 August 2017.Google Scholar
Ross, C, Sheridan, K. Storm flooding engulfs MD Anderson Cancer Center, canceling treatments for days. STAT. Published August 29, 2017. https://www.statnews.com/2017/08/29/md-anderson-harvey-flood/. Accessed September 30, 2019.Google Scholar
@kennalgriffin. Is there rescue operation for patients in hospitals? MD Anderson in #Houston is underwater!! #Harvey. https://twitter.com/kennalgriffin/status/901935477908848640. Posted 27 August 2017.Google Scholar
Bhatt, SP, Purohit, H, Hampton, A, et al. Assisting coordination during crisis: a domain ontology based approach to infer resource needs from tweets. Proceedings of the 2014 ACM conference on Web science. 2014:297298.CrossRefGoogle Scholar
Temnikova, I, Castillo, C, Vieweg, S. EMTerms 1.0: a terminological resource for crisis tweets. Proceedings of the ISCRAM 2015 Conference, Kristiansand, May 24-27. 2015. https://pdfs.semanticscholar.org/796c/d21b061bd16c0b185e3aa5c27b7581319bbd.pdf. Accessed September 14, 2017.Google Scholar
Munro, R, Manning, CD. Short message communications: users, topics, and in-language processing. Proceedings of the 2nd ACM Symposium on Computing for Development. 2012.CrossRefGoogle Scholar
Munro, R. Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol. Proceedings of the fifteenth conference on computational natural language learning. Association for Computational Linguistics. 2011.Google Scholar
Paul, MJ, Sarker, A, Brownstein, JS, et al. Social media mining for public health monitoring and surveillance. In: Altman, R, Dunker, AK, Hunter, L, et al., eds. Biocomputing 2016 Proceedings of the Pacific Symposium. Kohala Coast, Hawaii; 2016:468479.CrossRefGoogle Scholar
Freifeld, CC, Mandl, KD, Reis, BY, Brownstein, JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15(2):150157. doi:10.1197/jamia.M2544.CrossRefGoogle ScholarPubMed
Bennett, KJ, Olsen, JM, Harris, S, et al. The perfect storm of information: combining traditional and non-traditional data sources for public health situational awareness during hurricane response. PLoS Curr. 2013;5. doi:10.1371/currents.dis.d2800aa4e536b9d6849e966e91488003.CrossRefGoogle Scholar
World Health Organization (WHO). Emergency Response Framework. Geneva: WHO. Published 2013. https://www.who.int. Accessed December 17, 2018.Google Scholar
Reuter, C, Ludwig, T, Kaufhold, M-A, Spielhofer, T. Emergency services’ attitudes towards social media: a quantitative and qualitative survey across Europe. Int J Hum Comput Stud. 2016;96:91111. doi:10.1016/j.ijhcs.2016.03.005.Google Scholar
Luge, T. Lessons learned: social media monitoring during humanitarian crises. Published 2015. https://www.acaps.org/sites/acaps/files/resources/files/lessons_learned-social_media_monitoring_during_humanitarian_crises_september_2015.pdf. Accessed June 17, 2018.Google Scholar
Houston, JB, Hawthorne, J, Perreault, MF, et al. Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters. 2014;39(1):122. doi:10.1111/disa.12092.CrossRefGoogle ScholarPubMed
Markenson, D, Howe, L. American Red Cross Digital Operations Center (DigiDOC): an essential emergency management tool for the Digital Age. Disaster Med Public Health Prep. 2014;8(5):445451. doi: 10.1017/dmp.2014.102.CrossRefGoogle ScholarPubMed
Graham, MW, Avery, EJ, Park, S. The role of social media in local government crisis communications. Public Relat Rev. 2015;41:386394. doi:10.1016/j.pubrev.2015.02.001.CrossRefGoogle Scholar
Sutton, J, League, C, Sellnow, TL, Sellnow, DD. Terse messaging and public health in the midst of natural disasters: the case of the boulder floods. Health Commun. 2015;30(2):135143. doi:10.1080/10410236.2014.974124.CrossRefGoogle ScholarPubMed
Morrow, N, Mock, N, Papendieck, A, Kocmich, N. Independent evaluation of the Ushahidi Haiti Project. Published 2011. https://www.researchgate.net/publication/265059793. Accessed June 24, 2018.Google Scholar
Palen, L, Anderson, KM.Crisis informatics – new data for extraordinary times.” Science. 2016;353:224225. doi:10.1126/science.aag2579.CrossRefGoogle ScholarPubMed
Castillo, C. Big crisis data, social media in disasters and time-critical situations. 1st ed. Cambridge, UK: Cambridge University Press; 2016. doi:10.1017/CBO9781316476840.CrossRefGoogle Scholar
Imran, M, Castillo, C, Diaz, F, Vieweg, S. Processing social media messages in mass emergency: a survey. Published July 2014. http://arxiv.org/abs/1407.7071. Accessed December 30, 2018.Google Scholar
Purohit, H, Pandey, R. Intent mining for the good, bad, and ugly use of social web: concepts, methods, and challenges. In: Agarwal, N, Dokoohaki, N, Tokdemir, S, eds. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. Springer; 2019:318. doi:10.1007/978-3-319-94105-9_1 CrossRefGoogle Scholar
Allen, J. Natural language understanding. 2nd ed. Pearson. Published 1995. https://books.google.com/books/about/Natural_Language_Understanding_2_E.html?id=l0DOH4NHneIC. Accessed December 30, 2018.Google Scholar
Purohit, H, Dong, G, Shalin, V, et al. Intent classification of short-text on social media. In: 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). IEEE; 2015:222228. doi:10.1109/SmartCity.2015.75.CrossRefGoogle Scholar