Carlo Alberto Bono, Barbara Pernici, Jose Luis Fernandez-Marquez, Amudha Ravi Shankar, Mehmet Oguz Mülâyim, & Edoardo Nemni. (2022). TriggerCit: Early Flood Alerting using Twitter and Geolocation – A Comparison with Alternative Sources. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 674–686). Tarbes, France.
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. The system respectively returned a large scale and a local scale alert, both in a timely manner and accompanied by a valid geographical description, while providing information complementary to existing disaster alert mechanisms.
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Sara Barozzi, Jose Luis Fernandez Marquez, Amudha Ravi Shankar, & Barbara Pernici. (2019). Filtering images extracted from social media in the response phase of emergency events. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: The use of social media to support emergency operators in the first hours of the response phases can improve the
quality of the information available and awareness on ongoing emergency events. Social media contain both textual
and visual information, in the form of pictures and videos. The problem related to the use of social media posts
as a source of information during emergencies lies in the difficulty of selecting the relevant information among
a very large amount of irrelevant information. In particular, we focus on the extraction of images relevant to an
event for rapid mapping purpose. In this paper, a set of possible filters is proposed and analyzed with the goal of
selecting useful images from posts and of evaluating how precision and recall are impacted. Filtering techniques,
which include both automated and crowdsourced steps, have the goal of providing better quality posts and easy
manageable data volumes both to emergency responders and rapid mapping operators. The impact of the filters on
precision and recall in extracting relevant images is discussed in the paper in two different case studies.
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