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Creators/Authors contains: "Kapodistria, Angeliki"

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  1. ; ; ; ; (, 52nd Asilomar Conference on Signals, Systems, and Computers)
    Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real-time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near-real-time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable. 
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    Full Text Available 
  2. ; ; ; ; (, Asilomar Conference on Signals, Systems, and Computers)
    Participatory civil issue monitoring has emerged as an easy way for concerned citizens to report problems to their local government. For reported issues to be timely processed and addressed however, accurate, online and real–time processing methods to infer issue types are necessary. To address this challenge, we propose a computational, near–real–time civil issue reports processing method to estimate the actual issue from ambiguous and/or complementary information accurately and efficiently. We demonstrate the effectiveness of the proposed approach using a real-world dataset from SeeClickFix. We show that our approach is both highly accurate and scalable. 
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    Accepted Manuscript Full Text Available