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Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
Various domain users are increasingly leveraging real-time social media ...
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Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
In contrast to much previous work that has focused on location classific...
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Geovisual Analytics and Interactive Machine Learning for Situational Awareness
The first responder community has traditionally relied on calls from the...
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Location reference identification from tweets during emergencies: A deep learning approach
Twitter is recently being used during crises to communicate with officia...
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A personal model of trumpery: Deception detection in a real-world high-stakes setting
Language use reveals information about who we are and how we feel1-3. On...
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Characterization of citizens using word2vec and latent topic analysis in a large set of tweets
With the increasing use of the Internet and mobile devices, social netwo...
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Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Storm
Twitter is a popular social network platform where users can interact an...
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City-level Geolocation of Tweets for Real-time Visual Analytics
Real-time tweets can provide useful information on evolving events and situations. Geotagged tweets are especially useful, as they indicate the location of origin and provide geographic context. However, only a small portion of tweets are geotagged, limiting their use for situational awareness. In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We provide computational evaluations to demonstrate the superiority and utility of our geolocation prediction model within an interactive system.
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