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Incorporating Content Beyond Text: A High Reliable Twitter-Based Disaster Information System

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Computational Data and Social Networks (CSoNet 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11917))

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Abstract

Social media is a valuable information source with high-volume and real-time data. It has been used in a great number of event detection applications, especially in disaster information system. However, most of the systems only extract textual content. In this paper, we present an infrastructure pipeline of disaster information system using Twitter data. Landslide is used as an example for the demonstration purpose. To further improve the quality of the detected events, the pipeline integrates both textual and imagery content from tweets in hope to fully utilize the information. The text classifier is built to remove noises, which can achieve 0.92 F1-score in classifying individual messages. The image classifier is constructed by fine-tuning pretrained VGG-F network, which can achieve 90% accuracy. The image classifier serves as a verifier in the pipeline to reject or confirm the detected events. The evaluation indicates that this verifier can significantly reduce false positive events.

Supported by the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018BDKFJJ002).

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Author information

Authors and Affiliations

  1. Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China

    Qixuan Hou

  2. Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Qixuan Hou

  3. Data-Driven Intelligence Research (DIR) Laboratory, Kennesaw State University, Atlanta, GA, 30060, USA

    Meng Han

Authors
  1. Qixuan Hou
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  2. Meng Han
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Correspondence to Meng Han .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Andrea Tagarelli

  2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Hanghang Tong

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Hou, Q., Han, M. (2019). Incorporating Content Beyond Text: A High Reliable Twitter-Based Disaster Information System. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-34980-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34979-0

  • Online ISBN: 978-3-030-34980-6

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