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Microblogging sites such as Twitter can play a vital role in spreading information during " natural " or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner.(More)
Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during(More)
During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We(More)
In wireless sensor and actor networks maintaining inter-actor connectivity is very important in mission-critical applications where actors have to quickly plan optimal coordinated response to detected events. Failure of one or multiple actors may partition the inter-actor network into disjoint segments, and thus hinders the network operation. Autonomous(More)
We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters(More)
In this demonstration, we present ResEval Mash, a mashup platform for research evaluation, i.e., for the assessment of the productivity or quality of researchers, teams, institutions, journals, and the like - a topic most of us are acquainted with. The platform is specifically tailored to the need of sourcing data about scientific publications and(More)
An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which(More)
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:(More)