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Twitter-Based Disaster Response Using Recurrent Nets

Twitter-Based Disaster Response Using Recurrent Nets

Rabindra Lamsal, T. V. Vijay Kumar
Copyright: © 2023 |Pages: 20
ISBN13: 9781668471456|ISBN10: 1668471450|EISBN13: 9781668471463
DOI: 10.4018/978-1-6684-7145-6.ch031
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MLA

Lamsal, Rabindra, and T. V. Vijay Kumar. "Twitter-Based Disaster Response Using Recurrent Nets." Research Anthology on Managing Crisis and Risk Communications, edited by Information Resources Management Association, IGI Global, 2023, pp. 613-632. https://doi.org/10.4018/978-1-6684-7145-6.ch031

APA

Lamsal, R. & Kumar, T. V. (2023). Twitter-Based Disaster Response Using Recurrent Nets. In I. Management Association (Ed.), Research Anthology on Managing Crisis and Risk Communications (pp. 613-632). IGI Global. https://doi.org/10.4018/978-1-6684-7145-6.ch031

Chicago

Lamsal, Rabindra, and T. V. Vijay Kumar. "Twitter-Based Disaster Response Using Recurrent Nets." In Research Anthology on Managing Crisis and Risk Communications, edited by Information Resources Management Association, 613-632. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7145-6.ch031

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Abstract

Twitter has become the major source of data for the research community working on the social computing domain. The microblogging site receives millions of tweets every day on its platform. Earlier studies have shown that during any disaster, the frequency of tweets specific to an event grows exponentially, and these tweets, if monitored, processed, and analyzed, can contain actionable information relating to the event. However, during disasters, the number of tweets can be in the hundreds of thousands thereby necessitating the design of a semi-automated artificial intelligence-based system that can extract actionable information based on which steps can be taken for effective disaster response. This paper proposes a Twitter-based disaster response system that uses recurrent nets for training a classifier on a disaster specific tweets dataset. The proposed system would enable timely dissemination of information to various stakeholders so that timely response and proactive measures can be taken in order to reduce the severe consequences of disasters. Experimental results show that the recurrent nets outperform the traditional machine learning algorithms with regard to accuracy in classifying disaster-specific tweets.

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