Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-16T19:29:31.724Z Has data issue: false hasContentIssue false

Social Media Usage During Disasters: Exploring the Impact of Location and Distance on Online Engagement

Published online by Cambridge University Press:  01 August 2019

Qing Deng
Affiliation:
Institute of Public Safety Research, Tsinghua University, China
Yi Liu
Affiliation:
Public Order School, People’s Public Security University of China, China
Xiaodong Liu
Affiliation:
Public Order School, People’s Public Security University of China, China
Hui Zhang*
Affiliation:
Institute of Public Safety Research, Tsinghua University, China
Xiaolong Deng
Affiliation:
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, China
*
Correspondence and reprint requests to Hui Zhang, Room 1009, Liuqing Builiding, Tsinghua University, Haidian District, Beijing, China 100084 (e-mail: zhhui@mail.tsinghua.edu.cn).

Abstract

Social media play an important role in emergency management. The location of citizens and distance from a disaster influence the social media usage patterns. Using the Tianjin Port Explosion, we apply the correlation analysis and regression analysis to explore the relationship between online engagement and location. Citizens’ online engagement is estimated by social media. Three dimensions of the psychological distance – spatial, temporal, and social distances – are applied to measure the effects of location and distance. Online engagement is negatively correlated to such 3 kinds of the distance, which indicates that citizens may pay less attention to a disaster that happens at a far away location and at an area of less interaction or at a relatively long period of time. Furthermore, a linear model is proposed to measure the psychological distance. The quantification relationship between online engagement and psychological distance is discussed. The result enhances our understanding of social media usage patterns related to location and distance. The study gives a new insight on situation awareness, decision-making during disasters.

Type
Original Research
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

Takahashi, B, Tandoc, EC, Carmichael, C.Communicating on Twitter during a disaster: an analysis of tweets during Typhoon Haiyan in the Philippines. Comput Human Behav. 2015;50:392-398. doi: 10.1016/j.chb.2015.04.020.Google Scholar
Wong, TC, Chan, HK, Lacka, E.An ANN-based approach of interpreting user-generated comments from social media. Appl Soft Comput. 2017;52:1169-1180. doi: 10.1016/j.asoc.2016.09.011.CrossRefGoogle Scholar
Hadi, TA, Fleshler, K.Integrating social media monitoring into public health emergency response operations. Disaster Med Public Health Prep. 2016;10(05):775-780. doi: 10.1017/dmp.2016.39.CrossRefGoogle ScholarPubMed
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. doi: 10.1007/s11069-016-2327-8.Google Scholar
Hossain, L, Kam, D, Kong, F, Wigand, RT.Social media in Ebola outbreak. Epidemiol Infect. 2017;2016:2136-2143. doi: 10.1017/S095026881600039X.Google Scholar
Jamali, M, Nejat, A, Ghosh, S, et al. Social media data and post-disaster recovery. Int J Inf Manage. 2019;44:25-37.Google Scholar
Wukich, C.Government social media messages across disaster phases. J Contingencies Cris Manag. 2016;24(4):230-242. doi: 10.1111/1468-5973.12119.Google Scholar
Xu, X, Yin, X, Chen, X.A large-group emergency risk decision method based on data mining of public attribute preferences. Knowledge-Based Syst. 2019;163:495-509. doi: 10.1016/j.knosys.2018.09.010.Google Scholar
Ma, L.What drives the adoption of social media applications by the public sector? Int J Public Adm Digit Age. 2016;3(4):76-93. doi: 10.4018/IJPADA.2016100106.Google Scholar
Ngamassi, L. Examining the role of social media in disaster management from an attribution theory perspective. Proc ISCRAM 2016 Conference – Rio de Janeiro, Brazil, May 2016.Google Scholar
Díaz, P, Carroll, JM, Aedo, I. Coproduction as an approach to technology-mediated citizen participation in emergency management. Futur Internet. 2016;8(3). doi: 10.3390/fi8030041.Google Scholar
Simon, T, Goldberg, A, Adini, B.Socializing in emergencies – a review of the use of social media in emergency situations. Int J Inf Manage. 2015;35(5):609-619. doi: 10.1016/j.ijinfomgt.2015.07.001.CrossRefGoogle Scholar
Kumar, A, Singh, JP.Location reference identification from tweets during emergencies: a deep learning approach. Int J Disaster Risk Reduct. 2019;33:365-375. doi: 10.1016/j.ijdrr.2018.10.021.Google Scholar
Starbird, K, Dailey, D, Walker, AH, et al. Social media, public participation, and the 2010 BP Deepwater Horizon oil spill. Hum Ecol Risk Assess. 2015;21(3):605-630. doi: 10.1080/10807039.2014.947866.Google Scholar
Chandran, S, Menon, G.When a day means more than a year: effects of temporal framing on judgments of health risk. J Consum Res. 2004;31(2):375-389. doi: 10.1086/422116.Google Scholar
Huang, N, Burtch, G, Hong, Y, Polman, E.Effects of multiple psychological distances on construal and consumer evaluation: a field study of online reviews. J Consum Psychol. 2016;26(4):474-482. doi: 10.1016/j.jcps.2016.03.001.Google Scholar
Trope, Y, Liberman, N.Construal-level theory of psychological distance. Psychol Rev. 2010;117(2):440-463. doi: 10.1037/a0018963.CrossRefGoogle ScholarPubMed
Liberman, N, Trope, Y, Stephan, E.Social Psychology: Handbook of Basic Principles. New York: Guilford Press; 2007.Google Scholar
Liberman, N, Trope, Y.Traversing psychological distance. Trends Cogn Sci. 2014;18(7):364-369. doi: 10.1016/j.tics.2014.03.001.Google ScholarPubMed
Smith, A, Schlozman, KL, Verba, S.The Internet and civic engagement. Pew Res Center Internet Am Life Proj. 2009;67(3):311-334. doi: 10.1086/376947.Google Scholar
Kortelainen, T, Katvala, M.“Everything is plentiful-Except attention.” Attention data of scientific journals on social web tools. J Informetr. 2012;6(4):661-668. doi: 10.1016/j.joi.2012.06.004.Google Scholar
Knight, SR.Social media and online attention as an early measure of the impact of research in solid organ transplantation. Transplantation. 2014;98(5):490-496. doi: 10.1097/TP.0000000000000307.CrossRefGoogle ScholarPubMed
Van Lent, LGG, Sungur, H, Kunneman, FA, et al.Too far to care? Measuring public attention and fear for Ebola using Twitter. J Med Internet Res. 2017;19(6):1-10. doi: 10.2196/jmir.7219.Google ScholarPubMed
Strekalova, YA.Health risk information engagement and amplification on social media. Heal Educ Behav. 2017;44(2):332-339. doi: 10.1177/1090198116660310.Google ScholarPubMed
Xhafa, F, Patnaik, S, Yu, Z. Systems I. Recent developments in intelligent systems and interactive applications. In: Proceedings of the International Conference on Intelligent and Interactive Systems and Applications (IISA2016); 2016.CrossRefGoogle Scholar
Liu, Y, Wang, B, Wu, B, et al. Characterizing super-spreading in microblog: an epidemic-based information propagation model. Physica A. 2016;463:202-218. doi: 10.1016/j.physa.2016.07.022.Google ScholarPubMed
Snefjella, B, Kuperman, V.Concreteness and psychological distance in natural language use. Psychol Sci. 2015;26(9):1449-1460. doi: 10.1177/0956797615591771.CrossRefGoogle ScholarPubMed
Carmi, N, Kimhi, S.Further than the eye can see: psychological distance and perception of environmental threats. Hum Ecol Risk Assess An Int J. 2015;21(8):2239-2257. doi: 10.1080/10807039.2015.1046419.CrossRefGoogle Scholar
Liberman, N, Trope, Y, McCrea, SM, Sherman, SJ.The effect of level of construal on the temporal distance of activity enactment. J Exp Soc Psychol. 2007;43(1):143-149. doi: 10.1016/j.jesp.2005.12.009.Google Scholar
Fujita, K, Henderson, MD, Eng, J, et al. Spatial distance and mental construal of social events. Psychol Sci. 2006;17(4):278-280. doi: 10.1111/j.1467-9280.2006.01698.x.Google ScholarPubMed
Carmi, N, Bartal, E.Perception of environmental threat in the shadow of war: the effect of future orientation. Hum Ecol Risk Assess An Int J. 2014;20(3):872-886. doi: 10.1080/10807039.2013.798217.Google Scholar
Ayazi, T, Lien, L, Eide, A, et al. Community attitudes and social distance towards the mentally ill in South Sudan: a survey from a post-conflict setting with no mental health services. Soc Psychiatry Psychiatr Epidemiol. 2014;49(5):771-780. doi: 10.1007/s00127-013-0775-y.Google ScholarPubMed
Boothby, EJ, Smith, LK, Clark, MS, Bargh, JA.Psychological distance moderates the amplification of shared experience. Personal Soc Psychol Bull. 2016;42(10):1431-1444. doi: 10.1177/0146167216662869.CrossRefGoogle ScholarPubMed
Liviatan, I, Trope, Y, Liberman, N.Interpersonal similarity as a social distance dimension: implications for perception of others’ actions. J Exp Soc Psychol. 2008;44(5):1256-1269. doi: 10.1016/j.jesp.2008.04.007.Google ScholarPubMed
Darke, PR, Brady, MK, Benedicktus, RL, Wilson, AE.Feeling close from afar: the role of psychological distance in offsetting distrust in unfamiliar online retailers. J Retail. 2016;92(3):287-299. doi: 10.1016/j.jretai.2016.02.001.CrossRefGoogle Scholar
Lim, S, Cha, SY, Park, C, et al. Getting closer and experiencing together: antecedents and consequences of psychological distance in social media-enhanced real-time streaming video. Comput Human Behav. 2012;28(4):1365-1378. doi: 10.1016/j.chb.2012.02.022.Google Scholar
Huang, P, Zhang, J.Facts related to August 12, 2015 explosion accident in Tianjin, China. Process Saf Prog. 2015;34(4):313-314. doi: 10.1002/prs.11789.Google Scholar
Chung, YS, Kim, HS.On the August 12, 2015 occurrence of explosions and fires in Tianjin, China, and the atmospheric impact observed in central Korea. Air Qual Atmos Heal. 2015;8(6):521-532. doi: 10.1007/s11869-015-0371-2.CrossRefGoogle 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. doi: 10.1007/s11069-016-2484-9.Google Scholar