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Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

Author

Listed:
  • Abhinav Kumar

    (Siksha ‘O’ Anusanshan (Deemed to be University)
    National Institute of Technology)

  • Jyoti Prakash Singh

    (National Institute of Technology)

  • Nripendra P. Rana

    (Qatar University)

  • Yogesh K. Dwivedi

    (Swansea University
    Symbiosis International (Deemed University))

Abstract

During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:infosf:v:25:y:2023:i:4:d:10.1007_s10796-022-10309-x
    DOI: 10.1007/s10796-022-10309-x
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    References listed on IDEAS

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    1. Milad Mirbabaie & Christian Ehnis & Stefan Stieglitz & Deborah Bunker & Tanja Rose, 2021. "Digital Nudging in Social Media Disaster Communication," Information Systems Frontiers, Springer, vol. 23(5), pages 1097-1113, September.
    2. Viktor Pekar & Jane Binner & Hossein Najafi & Chris Hale & Vincent Schmidt, 2020. "Early detection of heterogeneous disaster events using social media," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(1), pages 43-54, January.
    3. Ayan Bandyopadhyay & Debasis Ganguly & Mandar Mitra & Sanjoy Kumar Saha & Gareth J.F. Jones, 2018. "An Embedding Based IR Model for Disaster Situations," Information Systems Frontiers, Springer, vol. 20(5), pages 925-932, October.
    4. 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.
    5. 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.
    6. Fang Liu & Dongming Xu, 2018. "Social Roles and Consequences in Using Social Media in Disasters: a Structurational Perspective," Information Systems Frontiers, Springer, vol. 20(4), pages 693-711, August.
    7. Girish Keshav Palshikar & Manoj Apte & Deepak Pandita, 2018. "Weakly Supervised and Online Learning of Word Models for Classification to Detect Disaster Reporting Tweets," Information Systems Frontiers, Springer, vol. 20(5), pages 949-959, October.
    8. 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.
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