|
Soudip Roy Chowdhury, Muhammad Imran, Muhammad Rizwan Asghar, Amer-Yahia, S., & Carlos Castillo. (2013). Tweet4act: Using incident-specific profiles for classifying crisis-related messages. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 834–839). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
|
|
|
Beibei Hu, Jan Hidders, Marc De Lignie, & Philipp Cimiano. (2011). A rule-based system for contextualized information delivery. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: When carrying out tasks, police officers need up-to-date information contextualized to their current situation to support them in decision making. The results of a previous user study with the aim of capturing the information requirements of police officers have led to the implementation of a rule-based system for contextualized information delivery. In this paper, we present the overall system and discuss how the various sources of information are modelled using ontologies. Our focus is on the formalism for expressing the rules and the engine executing those rules to decide which information is relevant for specific users. These declarative rules can be modified independently of the code executing them, thus providing a principled way to adapt the system to new domains. Quantitative evaluations on scenarios constructed in cooperation with police officers show that precision and recall levels of our system are satisfactory compared to other systems and that our system can be adapted to new scenarios with reasonable efforts.
|
|