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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Qixuan Hou, Meng Han

Erschienen in: Computational Data and Social Networks

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Subgraph-Based Adversarial Examples Against Graph-Based IoT Malware Detection Systems
Nächstes Kapitel Reduced-Bias Co-trained Ensembles for Weakly Supervised Cyberbullying Detection
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Metadaten
Titel
Incorporating Content Beyond Text: A High Reliable Twitter-Based Disaster Information System
verfasst von
Qixuan Hou
Meng Han
Copyright-Jahr
2019
Buch
Computational Data and Social Networks

Print ISBN: 978-3-030-34979-0

Electronic ISBN: 978-3-030-34980-6

Copyright-Jahr: 2019

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