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An Embedding Based IR Model for Disaster Situations

Author

Listed:
  • Ayan Bandyopadhyay

    (Indian Statistical Institute)

  • Debasis Ganguly

    (IBM Research)

  • Mandar Mitra

    (Indian Statistical Institute)

  • Sanjoy Kumar Saha

    (Jadavpur University)

  • Gareth J.F. Jones

    (Dublin City University)

Abstract

Twitter ( http://twitter.com ) is one of the most popular social networking platforms. Twitter users can easily broadcast disaster-specific information, which, if effectively mined, can assist in relief operations. However, the brevity and informal nature of tweets pose a challenge to Information Retrieval (IR) researchers. In this paper, we successfully use word embedding techniques to improve ranking for ad-hoc queries on microblog data. Our experiments with the ‘Social Media for Emergency Relief and Preparedness’ (SMERP) dataset provided at an ECIR 2017 workshop show that these techniques outperform conventional term-matching based IR models. In addition, we show that, for the SMERP task, our word embedding based method is more effective if the embeddings are generated from the disaster specific SMERP data, than when they are trained on the large social media collection provided for the TREC ( http://trec.nist.gov/ ) 2011 Microblog track dataset.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9847-6
    DOI: 10.1007/s10796-018-9847-6
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    Citations

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    Cited by:

    1. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.
    2. 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.
    3. 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.
    4. 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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