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Author (up) Zoha Sheikh; Hira Masood; Sharifullah Khan; Muhammad Imran
Title User-Assisted Information Extraction from Twitter During Emergencies Type Conference Article
Year 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal ISCRAM 2017
Volume Issue Pages 684-691
Keywords social media; disaster response; query expansion; supervised learning
Abstract Disasters and emergencies bring uncertain situations. People involved in such situations look for quick answers to their rapid queries. Moreover, humanitarian organizations look for situational awareness information to launch relief operations. Existing studies show the usefulness of social media content during crisis situations. However, despite advances in information retrieval and text processing techniques, access to relevant information on Twitter is still a challenging task. In this paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, we employee Word2vec embeddings to expand initial users queries and based on a relevance feedback mechanism we retrieve relevant messages on Twitter in real-time. Initial experiments and user studies performed using a real world disaster dataset show the significance of the proposed approach.
Address National University of Sciences and Technology, Islamabad, Pakistan; Qatar Computing Research Institute, HBKU Doha, Qatar
Corporate Author Thesis
Publisher Place of Publication Albi, France Editor Tina Comes, Frédérick Bénaben, Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Social Media Studies Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 1502
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