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

  • Qixuan Hou
  • Meng HanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

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.

Keywords

Social media Multimodal information Image classification 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Guizhou Provincial Key Laboratory of Public Big DataGuizhou UniversityGuiyangChina
  2. 2.Georgia Institute of TechnologyAtlantaUSA
  3. 3.Data-Driven Intelligence Research (DIR) LaboratoryKennesaw State UniversityAtlantaUSA

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