IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v20y2018i4d10.1007_s10796-017-9789-4.html
   My bibliography  Save this article

Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire

Author

Listed:
  • Babak Abedin

    (University of Technology Sydney)

  • Abdul Babar

    (University of Sydney)

Abstract

Social media plays a significant role in rapid propagation of information when disasters occur. Among the four phases of disaster management life cycle: prevention, preparedness, response, and recovery, this paper focuses on the use of social media during the response phase. It empirically examines the use of microblogging platforms by Emergency Response Organisations (EROs) during extreme natural events, and distinguishes the use of Twitter by EROs from digital volunteers during a fire hazard occurred in Australia state of Victoria in early February 2014. We analysed 7982 tweets on this event. While traditionally theories such as World System Theory and Institutional Theory focus on the role of powerful institutional information outlets, we found that platforms like Twitter challenge such notion by sharing the power between large institutional (e.g. EROs) and smaller non-institutional players (e.g. digital volunteers) in the dissemination of disaster information. Our results highlight that both large EROs and individual digital volunteers proactively used Twitter to disseminate and distribute fire related information. We also found that the contents of tweets were more informative than directive, and that while the total number of messages posted by top EROs was higher than the non-institutional ones, non-institutions presented a greater number of retweets.

Suggested Citation

  • Babak Abedin & Abdul Babar, 2018. "Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire," Information Systems Frontiers, Springer, vol. 20(4), pages 729-740, August.
  • Handle: RePEc:spr:infosf:v:20:y:2018:i:4:d:10.1007_s10796-017-9789-4
    DOI: 10.1007/s10796-017-9789-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-017-9789-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-017-9789-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jaeung Lee & Manish Agrawal & H. R. Rao, 2015. "Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013," Information Systems Frontiers, Springer, vol. 17(5), pages 997-1005, October.
    2. Seyedezahra Shadi Erfani & Babak Abedin & Yvette Blount, 2017. "The effect of social network site use on the psychological well-being of cancer patients," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(5), pages 1308-1322, May.
    3. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    2. Kerstin K. Zander & Jonas Rieskamp & Milad Mirbabaie & Mamoun Alazab & Duy Nguyen, 2023. "Responses to heat waves: what can Twitter data tell us?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3547-3564, April.
    3. Naim Kapucu & Ratna B. Dougherty & Yue Ge & Chris Zobel, 2023. "The use of documentary data for network analysis in emergency and crisis management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 425-445, March.
    4. Ziqiang Han & Mengfan Shen & Hongbing Liu & Yifan Peng, 2022. "Topical and emotional expressions regarding extreme weather disasters on social media: a comparison of posts from official media and the public," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    5. Paras Bhatt & Naga Vemprala & Rohit Valecha & Govind Hariharan & H. Raghav Rao, 2023. "User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse," Information Systems Frontiers, Springer, vol. 25(5), pages 1667-1682, October.
    6. Carine Dominguez-Péry & Rana Tassabehji & Lakshmi Narasimha Raju Vuddaraju & Vikhram Kofi Duffour, 2021. "Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster," Post-Print hal-04021179, HAL.
    7. Ghassan Beydoun & Sergiu Dascalu & Dale Dominey-Howes & Andrew Sheehan, 2018. "Disaster Management and Information Systems: Insights to Emerging Challenges," Information Systems Frontiers, Springer, vol. 20(4), pages 649-652, August.
    8. Ghassan Beydoun & Babak Abedin & José M. Merigó & Melanie Vera, 2019. "Twenty Years of Information Systems Frontiers," Information Systems Frontiers, Springer, vol. 21(2), pages 485-494, April.
    9. Josip Marić & Carlos Galera-Zarco & Marco Opazo-Basáez, 2022. "The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1003-1044, December.
    10. Franco Arolfo & Kevin Cortés Rodriguez & Alejandro Vaisman, 2022. "Analyzing the Quality of Twitter Data Streams," Information Systems Frontiers, Springer, vol. 24(1), pages 349-369, February.
    11. Qingqi Long & Ke Song, 2022. "Operational Performance Evaluation of E-government Microblogs Under Emergencies Based on a DEA Method," Information Systems Frontiers, Springer, vol. 24(5), pages 1-18, October.
    12. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    13. Yanxin Wang & Jian Li & Xi Zhao & Gengzhong Feng & Xin (Robert) Luo, 2020. "Using Mobile Phone Data for Emergency Management: a Systematic Literature Review," Information Systems Frontiers, Springer, vol. 22(6), pages 1539-1559, December.
    14. Emily Heaney & Laura Hunter & Angus Clulow & Devin Bowles & Sotiris Vardoulakis, 2021. "Efficacy of Communication Techniques and Health Outcomes of Bushfire Smoke Exposure: A Scoping Review," IJERPH, MDPI, vol. 18(20), pages 1-14, October.
    15. Fatuma Namisango & Kyeong Kang & Ghassan Beydoun, 2022. "How the Structures Provided by Social Media Enable Collaborative Outcomes: A Study of Service Co-creation in Nonprofits," Information Systems Frontiers, Springer, vol. 24(2), pages 517-535, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naveena Prakasam & Louisa Huxtable-Thomas, 2021. "Reddit: Affordances as an Enabler for Shifting Loyalties," Information Systems Frontiers, Springer, vol. 23(3), pages 723-751, June.
    2. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    3. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    4. Yu‐Ru Lin & Drew Margolin & Xidao Wen, 2017. "Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1580-1605, August.
    5. Leona Leišová-Svobodová & Sebastian Michel & Ilmar Tamm & Marie Chourová & Dagmar Janovska & Heinrich Grausgruber, 2019. "Diversity and Pre-Breeding Prospects for Local Adaptation in Oat Genetic Resources," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    6. Taffi, Marianna & Paoletti, Nicola & Liò, Pietro & Pucciarelli, Sandra & Marini, Mauro, 2015. "Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea," Ecological Modelling, Elsevier, vol. 306(C), pages 205-215.
    7. Lan, Jing & Liu, Zhen, 2019. "Social network effect on income structure of SLCP participants: Evidence from Baitoutan Village, China," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    8. Caterina Liberati & Massimiliano Marzo & Paolo Zagaglia & Paola Zappa, 2015. "Drivers of demand and supply in the Euro interbank market: the role of “Key Players” during the recent turmoil," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(3), pages 207-250, August.
    9. Caterina Liberati & Massimiliano Marzo & Paolo Zagaglia & Paola Zappa, 2012. "Structural Distortions in the Euro Interbank Market: The Role of 'Key Players' during the Recent Market Turmoil," Working Paper series 57_12, Rimini Centre for Economic Analysis.
    10. Marta Poblet & Esteban García-Cuesta & Pompeu Casanovas, 0. "Crowdsourcing roles, methods and tools for data-intensive disaster management," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    11. van Rijnsoever, Frank J. & van den Berg, Jesse & Koch, Joost & Hekkert, Marko P., 2015. "Smart innovation policy: How network position and project composition affect the diversity of an emerging technology," Research Policy, Elsevier, vol. 44(5), pages 1094-1107.
    12. Devendra Potnis & Macy Halladay, 2022. "Information practices of administrators for controlling information in an online community of new mothers in rural America," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(11), pages 1621-1640, November.
    13. Jiang, Meiling & Gao, Qingwu & Zhuang, Jun, 2021. "Reciprocal spreading and debunking processes of online misinformation: A new rumor spreading–debunking model with a case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    14. Konda, Bruhan & González‐Sauri, Mario & Cowan, Robin & Yashodha, Yashodha & Chellattan Veettil, Prakashan, 2021. "Social networks and agricultural performance: A multiplex analysis of interactions among Indian rice farmers," MERIT Working Papers 2021-030, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    15. Rauktis, Mary E. & McCarthy, Sharon & Krackhardt, David & Cahalane, Helen, 2010. "Innovation in child welfare: The adoption and implementation of Family Group Decision Making in Pennsylvania," Children and Youth Services Review, Elsevier, vol. 32(5), pages 732-739, May.
    16. Håvard Bergesen Dalen & Ørnulf Seippel, 2021. "Friends in Sports: Social Networks in Leisure, School and Social Media," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
    17. Han Zheng & Dion Hoe‐Lian Goh & Edmund Wei Jian Lee & Chei Sian Lee & Yin‐Leng Theng, 2022. "Understanding the effects of message cues on COVID‐19 information sharing on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 847-862, June.
    18. Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    19. Chul Woo Yoo, 2020. "An Exploration of the Role of Service Recovery in Negative Electronic Word-of-Mouth Management," Information Systems Frontiers, Springer, vol. 22(3), pages 719-734, June.
    20. David N. Fisher & Rolando Rodríguez-Muñoz & Tom Tregenza, 2016. "Comparing pre- and post-copulatory mate competition using social network analysis in wild crickets," Behavioral Ecology, International Society for Behavioral Ecology, vol. 27(3), pages 912-919.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:20:y:2018:i:4:d:10.1007_s10796-017-9789-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.