TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a comparison with alternative sources

• URL: http://arxiv.org/abs/2202.12014v2
• Date: Sat, 5 Mar 2022 12:23:40 GMT
• Title: TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a comparison with alternative sources
• Authors: Carlo Bono (1), Barbara Pernici (1), Jose Luis Fernandez-Marquez (2), Amudha Ravi Shankar (2), Mehmet O\u{g}uz M\"ul\^ayim (3), Edoardo Nemni (4 and 5) ((1) Politecnico di Milano, (2) University of Geneva, (3) Artificial Intelligence Research Institute IIIA-CSIC, (4) United Nations Satellite Centre UNOSAT, (5) United Nations Institute for Training and Research UNITAR)
• Abstract summary: Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. We propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.
• Score: 0.2603110718989132
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.

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