Facebook and Twitter save lives how social media helps When natural disasters occur

Abstract

Social media research during natural disasters has been presented as a tool to guide response and relief efforts in the disciplines of geography and computer sciences. This systematic review highlights the public health implications of social media use in the response phase of the emergency, assessing (1) how social media can improve the dissemination of emergency warning and response information during and after a natural disaster, and (2) how social media can help identify physical, medical, functional, and emotional needs after a natural disaster. We surveyed the literature using 3 databases and included 44 research articles. We found that analyses of social media data were performed using a wide range of spatiotemporal scales. Social media platforms were identified as broadcasting tools presenting an opportunity for public health agencies to share emergency warnings. Social media was used as a tool to identify areas in need of relief operations or medical assistance by using self-reported location, with map development as a common method to visualize data. In retrospective analyses, social media analysis showed promise as an opportunity to reduce the time of response and to identify the individuals’ location. Further research for misinformation and rumor control using social media is needed.

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. 2016;83(1):729–760.10.1007/s11069-016-2327-8CrossRefGoogle Scholar

Fung, IC, Tse, ZT, Fu, KW. The use of social media in public health surveillance. Western Pac surveill Response J. 2015;6(2):3–6.10.5365/wpsar.2015.6.1.019CrossRefGoogle ScholarPubMed

Kim, J, Bae, J, Hastak, M. Emergency information diffusion on online social media during storm Cindy in US. Int J Inf Manage. 2018;40:153–165.10.1016/j.ijinfomgt.2018.02.003CrossRefGoogle Scholar

Adams, J, Raeside, R, Khan, HTA. Research Methods for Business and Social Science Students. 2nd ed. New Delhi: Sage Publications Pvt. Ltd; 2014.Google Scholar

Wang, B, Zhuang, J. Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy. Nat Hazards. 2017;89(1):161–181.10.1007/s11069-017-2960-xCrossRefGoogle Scholar

Kiatpanont, R, Tanlamai, U, Chongstitvatana, P. Extraction of actionable information from crowdsourced disaster data. J Emerg Manag. 2016;14(6):377–390.10.5055/jem.2016.0302CrossRefGoogle ScholarPubMed

Ungvarsky, J.Sentiment Analysis. Pasadena, CA: Salem Press; 2017.Google Scholar

Abedin, B, Babar, A, Abbasi, A. Characterization of the use of social media in natural disasters: a systematic review. In: Proceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing IEEE Computer Society, Sydney, Australia; 2014.10.1109/BDCloud.2014.17CrossRefGoogle Scholar

Yu, M, Yang, C, Li, Y. Big data in natural disaster management: a review. Geosciences. 2018;8(5):165.10.3390/geosciences8050165CrossRefGoogle Scholar

Wang, Z, Ye, X. Social media analytics for natural disaster management. Int J Geograph Inf Sci. 2018;32(1):49–72.10.1080/13658816.2017.1367003CrossRefGoogle Scholar

Moher, D, Liberati, A, Tetzlaff, J, et al.Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.10.1371/journal.pmed.1000097CrossRefGoogle ScholarPubMed

Rodrigueza, RC, Estuar, MRJE. Social network analysis of a disaster behavior network: an agent-based modeling approach. Paper presented at: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); August 28-31, 2018.Google Scholar

Wu, D, Cui, Y. Disaster early warning and damage assessment analysis using social media data and geo-location information. Decis Support Syst. 2018;111:48–59.10.1016/j.dss.2018.04.005CrossRefGoogle Scholar

Liu, G-R, Mao, L-X, Wang, L-L, et al. Automotive prospective technology mining method based on big data content analysis. Paper presented at: 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA); April 28-30, 2017.Google Scholar

Deng, Q, Liu, Y, Zhang, H, et al.A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan. Nat Hazards. 2016;84(2):1241–1256.10.1007/s11069-016-2484-9CrossRefGoogle Scholar

Resch, B, Uslander, F, Havas, C. Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartogr Geogr Inf Sci. 2018;45(4):362–376.10.1080/15230406.2017.1356242CrossRefGoogle Scholar

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):100–124.10.1080/01431161.2015.1117684CrossRefGoogle Scholar

Avvenuti, M, Cresci, S, Del Vigna, F, Tesconi, M. Impromptu crisis mapping to prioritize emergency response. Computer. 2016;49(5):28–37.10.1109/MC.2016.134CrossRefGoogle Scholar

Avvenuti, M, Cimino, MG, Cresci, S, et al.A framework for detecting unfolding emergencies using humans as sensors. Springerplus. 2016;5:43.10.1186/s40064-016-1674-yCrossRefGoogle ScholarPubMed

Kim, J, Hastak, M. Social network analysis: characteristics of online social networks after a disaster. Int J Inf Manag. 2018;38(1):86–96.10.1016/j.ijinfomgt.2017.08.003CrossRefGoogle 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):135–143.10.1080/10410236.2014.974124CrossRefGoogle ScholarPubMed

Tang, Z, Zhang, L, Xu, F, et al.Examining the role of social media in California’s drought risk management in 2014. Nat Hazards. 2015;79(1):171–193.10.1007/s11069-015-1835-2CrossRefGoogle Scholar

Scott, KK, Errett, NA. Content, accessibility, and dissemination of disaster information via social media during the 2016 Louisiana floods. J Public Health Manag Pract. 2018;24(4):370–379.10.1097/PHH.0000000000000708CrossRefGoogle ScholarPubMed

Albris, K. The switchboard mechanism: how social media connected citizens during the 2013 floods in Dresden. J ContingCrisis Manag. 2018;26(3):350–357.10.1111/1468-5973.12201CrossRefGoogle Scholar

Wang, Z, Ye, X, Tsou, M-H. Spatial, temporal, and content analysis of Twitter for wildfire hazards. Nat Hazards. 2016;83(1):523–540.10.1007/s11069-016-2329-6CrossRefGoogle Scholar

Grasso, V, Crisci, A. Codified hashtags for weather warning on Twitter: an Italian case study. PLoS Curr. 2016;8 pii: ecurrents.dis.967e71514ecb92402eca3bdc9b789529.Google ScholarPubMed

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. 2018. doi: 10.1097/PHH.0000000000000841.Google ScholarPubMed

Ramirez Plascencia, D, Ramirez Plascencia, J. e-Solidarity and exchange: the role of social media in public mexican response to Hurricane Patricia in 2015. Int J Public Admin Digit Age. 2017;4(3):1–10.10.4018/IJPADA.2017070101CrossRefGoogle Scholar

Yi, CJ, Kuri, M. The prospect of online communication in the event of a disaster. J Risk Res. 2016;19(7):951–963.10.1080/13669877.2015.1115424CrossRefGoogle 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.Google Scholar

Li, L, Zhang, Q, Tian, J, et al.Characterizing information propagation patterns in emergencies: a case study with Yiliang Earthquake. Int J Inf Manag. 2018;38(1):34–41.10.1016/j.ijinfomgt.2017.08.008CrossRefGoogle 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):e0150190.10.1371/journal.pone.0150190CrossRefGoogle ScholarPubMed

Cooper, GP Jr, Yeager, V, Burkle, FM Jr, et al.Twitter as a potential disaster risk reduction tool. Part III: evaluating variables that promoted regional Twitter use for at-risk populations during the 2013 Hattiesburg F4 tornado. PLoS Curr Disast. 2015;7.Google ScholarPubMed

Huang, Q, Xiao, Y. Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery. Isprs Int J Geo-Inf. 2015;4(3):1549–1568.10.3390/ijgi4031549CrossRefGoogle Scholar

Pohl, D, Bouchachia, A, Hellwagner, H. Online indexing and clustering of social media data for emergency management. Neurocomputing. 2016;172:168–179.10.1016/j.neucom.2015.01.084CrossRefGoogle Scholar

Stephenson, J, Vaganay, M, Coon, D, et al.The role of Facebook and Twitter as organisational communication platforms in relation to flood events in Northern Ireland. J Flood Risk Manag. 2018;11(3):339–350.10.1111/jfr3.12329CrossRefGoogle Scholar

Schiavo, R. Health Communiation from Theory to Practice San Francisco, CA: John Wiley & Sons; 2007.Google Scholar

Valente, TW. Social Networks and Health: Models, Methods, and Applications. New York: University Press Scholarship Online; 2010.10.1093/acprof:oso/9780195301014.001.0001CrossRefGoogle Scholar

Juliadi, R, Ardani, EG. The interactivity of twitwar among social media influencer and followers on Twitter. Int J Multicul Multireligious Underst. 2019(7):110.Google Scholar

Nath, RN, Priya, N, Rene Robin, CR. A social media content based location and situation analysis model for disaster management. Int J Public Admin Digital Age. 2017;4(3):42–52.10.4018/IJPADA.2017070104CrossRefGoogle Scholar

Bai, H, Yu, G. A Weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via Weibo text negative sentiment analysis. Nat Hazards. 2016;83(2):1177–1196.10.1007/s11069-016-2370-5CrossRefGoogle 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;28:758–767.10.1016/j.ijdrr.2018.02.003CrossRefGoogle Scholar

de Albuquerque, JP, Herfort, B, Brenning, A, et al.A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. Int J Geograph Inf Sci. 2015;29(4):667–689.10.1080/13658816.2014.996567CrossRefGoogle Scholar

Kryvasheyeu, Y, Chen, H, Obradovich, N, et al.Rapid assessment of disaster damage using social media activity. Sci Adv. 2016;2(3):e1500779.10.1126/sciadv.1500779CrossRefGoogle 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).Google Scholar

Zahra, K, Ostermann, FO, Purves, RS. Geographic variability of Twitter usage characteristics during disaster events. Geo Spat Inf Sci. 2017;20(3):231–240.10.1080/10095020.2017.1371903CrossRefGoogle Scholar

Zou, L, Lam, NSN, Cai, H, et al.Mining Twitter data for improved understanding of disaster resilience. Ann Am Assoc Geogr. 2018;108(5):1422–1441.Google Scholar

Tim, Y, Pan, SL, Ractham, P, et al.Digitally enabled disaster response: the emergence of social media as boundary objects in a flooding disaster. Inf Syst J. 2017;27(2):197–232.10.1111/isj.12114CrossRefGoogle Scholar

Gul, S, Shah, TA, Ahad, M, et al.Twitter sentiments related to natural calamities Analysing tweets related to the Jammu and Kashmir floods of 2014. Electron Library. 2018;36(1):38–54.10.1108/EL-12-2015-0244CrossRefGoogle Scholar

Ragini, JR, Anand, PMR, Bhaskar, V. Big data analytics for disaster response and recovery through sentiment analysis. Int J Inf Manag. 2018;42:13–24.10.1016/j.ijinfomgt.2018.05.004CrossRefGoogle Scholar

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):287–312.10.1007/s10844-016-0404-9CrossRefGoogle Scholar

Xu, Z, Zhang, H, Sugumaran, V, et al.Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP J Wirel Commun Netw. 2016;44. doi: 10.1186/s13638-016-0553-0.Google Scholar

Sherchan, W, Pervin, S, Butler, CJ, et al.Harnessing Twitter and Instagram for disaster management. IBM J Res Dev. 2017;61(6):8:1–8:12.10.1147/JRD.2017.2729238CrossRefGoogle Scholar

Avvenuti, M, Cresci, S, La Polla, MN, et al.Nowcasting of earthquake consequences using big social data. IEEE Internet Comput. 2017;21(6):37–45.10.1109/MIC.2017.4180834CrossRefGoogle Scholar

Fohringer, J, Dransch, D, Kreibich, H, et al.Social media as an information source for rapid flood inundation mapping. Nat Hazards Earth Syst Sci. 2015;15(12):2725–2738.10.5194/nhess-15-2725-2015CrossRefGoogle Scholar

Li, Z, Wang, C, Emrich, CT, et al.A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods. Cartogr Geogr Inf Sci. 2017;45(2):97–110.10.1080/15230406.2016.1271356CrossRefGoogle Scholar

Wang, Y, Taylor, JE. Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake. Nat Hazards. 2018;92(2):907–925.10.1007/s11069-018-3231-1CrossRefGoogle Scholar

Federal Emergency Management Agency. Introduction to Crisis, Disaster, and Risk Management Concepts. 2018:25.Google Scholar

Lee, H, Oh, HJ. Normative mechanism of rumor dissemination on Twitter. Cyberpsychol Behav Soc Netw. 2017;20(3):164–171.10.1089/cyber.2016.0447CrossRefGoogle ScholarPubMed

Harris, JK, Mueller, NL, Snider, D. Social media adoption in local health departments nationwide. Am J Public Health. 2013;103(9):1700–1707.10.2105/AJPH.2012.301166CrossRefGoogle ScholarPubMed

Liang, H, Shen, F, Fu, K-w. Privacy protection and self-disclosure across societies: a study of global Twitter users. New Media Soc. 2016;19(9):1476–1497.10.1177/1461444816642210CrossRefGoogle 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;7(1):75–87.10.1504/IJEM.2010.032046CrossRefGoogle Scholar

Jackson, AM, Mullican, LA, Tse, ZTH, Yin, J, et al.Unplanned closure of public schools in Michigan, 2015-2016: Cross-sectional study on rurality and digital data harvesting. J Sch Health. 2020. In Press.Google Scholar

How social media helps when natural disasters occur?

How is Twitter used by people affected by natural disasters?

How does Twitter help during a crisis emergency?

In what ways media can be helpful during a disaster?