Lise Ann St. Denis, Amanda Lee Hughes, Jeremy Diaz, Kylen Solvik, Maxwell B. Joseph, & Jennifer K. Balch. (2020). 'What I Need to Know is What I Don't Know!': Filtering Disaster Twitter Data for Information from Local Individuals. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 730–743). Blacksburg, VA (USA): Virginia Tech.
Abstract: We report on the design, development, and evaluation of a user labeling framework for social media monitoring by emergency responders. By labeling Twitter user accounts based on behavior and content, this novel approach identifies tweets from accounts belonging to Individuals generating Personalized content and captures information that might otherwise be missed. We evaluate the framework using training data from the 2018 Camp, Woolsey, and Hill fires. Approximately 30% of the Individual-Personalized tweets contain first-hand information, providing a rich stream of content for social media monitoring. Because it can quickly eliminate most redundant tweets, this framework could be a critical first step in an end-to-end information extraction pipeline. It may also generalize more easily for new disaster events since it relies on general user account attributes rather than tweet content. We conclude with next steps for refining and evaluating our framework in near real-time during a disaster response.
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Vitaveska Lanfranchi, Nadia Noori, & Tudor Sirbu. (2018). GPS-based solution for tracking and protecting humanitarians in conflict zones. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 334–349). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: The operational environment in which humanitarians operate is unstable and high-risk; when operating in such environments, time becomes a critical factor. Thus, real-time location systems (RTLS) are often deployed in the operational environment to provide awareness of the location of personnel and assets in real-time that would support an informed decision making in the event of responding to emergency. Whilst standard RTLS are very precise, they are not suitable to outdoor spaces; GPS position technology can be used to identify the location of objects and people and to track them. In this paper, first, we present a description of threat scenarios identified based on information from existing security incidents datasets and from interviews with aid workers and security professionals operating in high-risk regions. Second, we describe the implementation of a GPS-based real-time location tracking and alert system for humanitarians operating in conflict zones that supports the identified scenarios.
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