IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v283y2019i1d10.1007_s10479-017-2522-3.html
   My bibliography  Save this article

Event classification and location prediction from tweets during disasters

Author

Listed:
  • Jyoti Prakash Singh

    (National Institute of Technology Patna)

  • Yogesh K. Dwivedi

    (Swansea University Bay Campus)

  • Nripendra P. Rana

    (Swansea University Bay Campus)

  • Abhinav Kumar

    (National Institute of Technology Patna)

  • Kawaljeet Kaur Kapoor

    (Business School, Brunel University London)

Abstract

Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

Suggested Citation

  • Jyoti Prakash Singh & Yogesh K. Dwivedi & Nripendra P. Rana & Abhinav Kumar & Kawaljeet Kaur Kapoor, 2019. "Event classification and location prediction from tweets during disasters," Annals of Operations Research, Springer, vol. 283(1), pages 737-757, December.
    • Handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2522-3
    DOI: 10.1007/s10479-017-2522-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2522-3
    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/s10479-017-2522-3?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. Linet Özdamar & Ediz Ekinci & Beste Küçükyazici, 2004. "Emergency Logistics Planning in Natural Disasters," Annals of Operations Research, Springer, vol. 129(1), pages 217-245, July.
    2. Yu Xiao & Qunying Huang & Kai Wu, 2015. "Understanding social media data for disaster 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. 79(3), pages 1663-1679, December.
    3. Thomas Heverin & Lisl Zach, 2012. "Use of microblogging for collective sense-making during violent crises: A study of three campus shootings," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 34-47, January.
    4. Thomas Heverin & Lisl Zach, 2012. "Use of microblogging for collective sense‐making during violent crises: A study of three campus shootings," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 34-47, January.
    5. Anonymous, 2013. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 8(2), pages 129-130, November.
    6. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    7. M. Ülkü & Kathryn Bell & Stephanie Wilson, 2015. "Modeling the impact of donor behavior on humanitarian aid operations," Annals of Operations Research, Springer, vol. 230(1), pages 153-168, July.
    8. Anonymous, 2013. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 8(3), pages 243-243, December.
    9. Lei Lei & Michael Pinedo & Lian Qi & Shengbin Wang & Jian Yang, 2015. "Personnel scheduling and supplies provisioning in emergency relief operations," Annals of Operations Research, Springer, vol. 235(1), pages 487-515, December.
    10. Turoff, Murray & Hiltz, Starr Roxanne & Bañuls, Víctor A. & Van Den Eede, Gerd, 2013. "Multiple perspectives on planning for emergencies: An introduction to the special issue on planning and foresight for emergency preparedness and management," Technological Forecasting and Social Change, Elsevier, vol. 80(9), pages 1647-1656.
    11. Jomon Paul & Govind Hariharan, 2012. "Location-allocation planning of stockpiles for effective disaster mitigation," Annals of Operations Research, Springer, vol. 196(1), pages 469-490, July.
    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. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    3. Paul Souma Kanti & Riaz Sadia & Das Suchismita, 2022. "Artificial intelligence adoption in supply chain risk management: Scale development and validation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 12(2), pages 15-32.
    4. Prabhsimran Singh & Surleen Kaur & Abdullah M. Baabdullah & Yogesh K. Dwivedi & Sandeep Sharma & Ravinder Singh Sawhney & Ronnie Das, 2023. "Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter," Information Systems Frontiers, Springer, vol. 25(1), pages 199-219, February.
    5. Duan, Huijue Kelly & Vasarhelyi, Miklos A. & Codesso, Mauricio & Alzamil, Zamil, 2023. "Enhancing the government accounting information systems using social media information: An application of text mining and machine learning," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    6. Guizhe Song & Degen Huang, 2021. "A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data," Future Internet, MDPI, vol. 13(7), pages 1-15, June.
    7. Jamal Al Qundus & Kosai Dabbour & Shivam Gupta & Régis Meissonier & Adrian Paschke, 2022. "Wireless sensor network for AI-based flood disaster detection," Annals of Operations Research, Springer, vol. 319(1), pages 697-719, December.
    8. Sameer Kumar & Chong Xu & Nidhi Ghildayal & Charu Chandra & Muer Yang, 2022. "Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 823-851, December.
    9. Abhinav Kumar & Jyoti Prakash Singh & Nripendra P. Rana & Yogesh K. Dwivedi, 2023. "Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster," Information Systems Frontiers, Springer, vol. 25(4), pages 1589-1604, August.
    10. Sachin Modgil & Rohit Kumar Singh & Cyril Foropon, 2022. "Quality management in humanitarian operations and disaster relief management: a review and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 1045-1098, December.
    11. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    12. Choi, Tsan-Ming, 2020. "Innovative “Bring-Service-Near-Your-Home” operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    13. Abhinav Kumar & Jyoti Prakash Singh & Yogesh K. Dwivedi & Nripendra P. Rana, 2022. "A deep multi-modal neural network for informative Twitter content classification during emergencies," Annals of Operations Research, Springer, vol. 319(1), pages 791-822, December.
    14. Zha, Wenbin & Ye, Qian & Li, Jian & Ozbay, Kaan, 2023. "A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    15. Serge Nyawa & Dieudonné Tchuente & Samuel Fosso-Wamba, 2024. "COVID-19 vaccine hesitancy: a social media analysis using deep learning," Annals of Operations Research, Springer, vol. 339(1), pages 477-515, August.
    16. Mihalis Giannakis & Rameshwar Dubey & Shishi Yan & Konstantina Spanaki & Thanos Papadopoulos, 2022. "Social media and sensemaking patterns in new product development: demystifying the customer sentiment," Annals of Operations Research, Springer, vol. 308(1), pages 145-175, January.

    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. Panagiotopoulos, Panos & Barnett, Julie & Bigdeli, Alinaghi Ziaee & Sams, Steven, 2016. "Social media in emergency management: Twitter as a tool for communicating risks to the public," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 86-96.
    2. Margherita, Alessandro & Elia, Gianluca & Klein, Mark, 2021. "Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Muhammed Kursad Ozlen, 2021. "Managers’ Attitude and Organizational Culture for Social Media Use in Supply Chain Efficiency and Responsiveness," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 20(3), pages 477-493, September.
    4. Robert Fabac & Davor Djalog & Vinko Zebic, 2015. "Organizing for Emergencies - Issues in Wildfire Fighting in Croatia," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 13(1), pages 99-116.
    5. Miguel Ramirez de la Huerga & Víctor A. Bañuls Silvera & Murray Turoff & Manuel Rincón Roldan, 2019. "Evaluation Tool for Business Success," Working Papers 19.01, Universidad Pablo de Olavide, Department of Business Organization and Marketing (former Department of Business Administration).
    6. Ashish K. Rathore & Arpan K. Kar & P. Vigneswara Ilavarasan, 2017. "Social Media Analytics: Literature Review and Directions for Future Research," Decision Analysis, INFORMS, vol. 14(4), pages 229-249, December.
    7. Margherita, Alessandro & Heikkilä, Marikka, 2021. "Business continuity in the COVID-19 emergency: A framework of actions undertaken by world-leading companies," Business Horizons, Elsevier, vol. 64(5), pages 683-695.
    8. Ranjana Raghunathan, 2022. "Everyday Intimacies and Inter-Ethnic Relationships: Tracing Entanglements of Gender and Race in Multicultural Singapore," Sociological Research Online, , vol. 27(1), pages 77-94, March.
    9. Songsore, Emmanuel & Buzzelli, Michael, 2014. "Social responses to wind energy development in Ontario: The influence of health risk perceptions and associated concerns," Energy Policy, Elsevier, vol. 69(C), pages 285-296.
    10. Tapsuwan, Sorada & Polyakov, Maksym & Bark, Rosalind & Nolan, Martin, 2015. "Valuing the Barmah–Millewa Forest and in stream river flows: A spatial heteroskedasticity and autocorrelation consistent (SHAC) approach," Ecological Economics, Elsevier, vol. 110(C), pages 98-105.
    11. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    12. Gigi Foster, 2020. "The behavioural economics of government responses to COVID-19," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(S3), pages 11-43, December.
    13. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    14. Audoly, Richard & Vogt-Schilb, Adrien & Guivarch, Céline & Pfeiffer, Alexander, 2018. "Pathways toward zero-carbon electricity required for climate stabilization," Applied Energy, Elsevier, vol. 225(C), pages 884-901.
    15. Gerards, Ruud & Welters, Ricardo, 2016. "Impact of financial pressure on unemployed job search, job find success and job quality," ROA Research Memorandum 008, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    16. Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    17. Cailong Xu & Ruidong Li & Wenwen Song & Tingting Wu & Shi Sun & Shuixiu Hu & Tianfu Han & Cunxiang Wu, 2021. "Responses of Branch Number and Yield Component of Soybean Cultivars Tested in Different Planting Densities," Agriculture, MDPI, vol. 11(1), pages 1-12, January.
    18. Caitlin Robinson & Stefan Bouzarovski & Sarah Lindley, 2018. "Underrepresenting neighbourhood vulnerabilities? The measurement of fuel poverty in England," Environment and Planning A, , vol. 50(5), pages 1109-1127, August.
    19. Michaela Haase & Emmanuel Raufflet, 2017. "Ideologies in Markets, Organizations, and Business Ethics: Drafting a Map: Introduction to the Special Issue," Journal of Business Ethics, Springer, vol. 142(4), pages 629-639, June.
    20. Rafael Alcadipani & Cíntia Rodrigues Oliveira Medeiros, 2020. "When Corporations Cause Harm: A Critical View of Corporate Social Irresponsibility and Corporate Crimes," Journal of Business Ethics, Springer, vol. 167(2), pages 285-297, November.

    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:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2522-3. 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.