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Mining Help Intent on Twitter During Disasters via Transfer Learning with Sparse Coding

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10899))

Abstract

Citizens share a variety of information on social media during disasters, including messages with the intentional behavior of seeking or offering help. Timely identification of such help intent can operationally benefit disaster management by aiding the information collection and filtering for response planning. Prior research on intent identification has developed supervised learning methods specific to a disaster using labeled messages from that disaster. However, rapidly acquiring a large set of labeled messages is difficult during a new disaster in order to train a supervised learning classifier. In this paper, we propose a novel transfer learning method for help intent identification on Twitter during a new disaster. This method efficiently transfers the knowledge of intent behavior from the labeled messages of the past disasters using novel Sparse Coding feature representation. Our experiments using Twitter data from four disaster events show the performance gain up to 15% in both F-score and accuracy over the baseline of popular Bag-of-Words representation. The results demonstrate the applicability of our method to assist realtime help intent identification in future disasters.

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Notes

  1. 1.

    https://www.npr.org/2013/01/09/168946170/thanks-but-no-thanks-when-post-disaster-donations-overwhelm.

  2. 2.

    Datasets are available upon request, for research purposes.

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Acknowledgment

Authors thank Rahul Pandey for discussion and proofreading as well as acknowledge the partial support from U.S. National Science Foundation (NSF) grant IIS-1657379. Opinions in this article are those of the authors and do not necessarily represent the official position or policies of the NSF.

Author information

Authors and Affiliations

  1. Computer Science Department, George Mason University, Fairfax, VA, USA

    Bahman Pedrood

  2. Information Sciences and Technology Department, George Mason University, Fairfax, VA, USA

    Hemant Purohit

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  1. Bahman Pedrood
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Correspondence to Bahman Pedrood .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, New York, USA

    Robert Thomson

  2. Bucknell University, Lewisburg, Pennsylvania, USA

    Christopher Dancy

  3. The Ohio State University, Columbus, Ohio, USA

    Ayaz Hyder

  4. University of Michigan–Flint, Flint, Michigan, USA

    Halil Bisgin

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Pedrood, B., Purohit, H. (2018). Mining Help Intent on Twitter During Disasters via Transfer Learning with Sparse Coding. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93371-9

  • Online ISBN: 978-3-319-93372-6

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