Abstract:
Sudden-onset emergencies such as natural or man-made disasters bring uncertainties in which time-critical information needs emerge from formal response organizations, affected communities and other concerned population. The growing adaption of Information and Communication Technologies (ICT) and Social Networks such as Twitter, Facebook has created numerous opportunities to disseminate and consume critical information during an on-going situation. However, time-critical analysis of high-velocity social media streams containing high-volume data involves solving multiple challenges including realtime parsing of brief and informal messages, handling information overload issues, and classifying, summarizing, and prioritizing different types of information. In this talk, I will present our work on solving some of these challenges.

Bio:
Muhammad Imran is a Research Scientist at the Qatar Computing Research Institute where he leads the Crisis Computing team. His interdisciplinary research focuses on natural language processing, text mining, human-computer interaction, and applied machine learning. Imran has published over 70 research papers in top-tier international conferences and journals including ACL, SIGIR, ICWSM, WWW, and ASONAM. Two of his papers received the Best Paper Award.

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Comunicaciones DCC