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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
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    Cited by:

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    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. Helen Ngonidzashe Serere & Bernd Resch & Clemens Rudolf Havas, 2023. "Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-19, March.
    13. 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).
    14. 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.
    15. Dhiraj Murthy & Sophia Elisavet Kurz & Tanvi Anand & Sonali Hornick & Nandhini Lakuduva & Jerry Sun, 2025. "Examining hurricane–related social media topics longitudinally and at scale: A transformer-based approach," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-22, January.
    16. 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).
    17. 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.
    18. 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.

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