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Source-Aware Crisis-Relevant Tweet Identification and Key Information Summarization

Published:27 August 2019Publication History
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Abstract

Twitter is an important source of information that people frequently contribute to and rely on for emerging topics, public opinions, and event awareness. Crisis-relevant tweets can potentially avail a magnitude of applications such as helping authorities and governments become aware of situations and thus offer better responses. One major challenge toward crisis-awareness in Twitter is to identify those tweets that are relevant to unseen crises. In this article, we propose an automatic labeling approach to distinguishing crisis-relevant tweets while differentiating source types (e.g., government or personal accounts) simultaneously. We first analyze and identify tweet-specific linguistic, sentimental, and emotional features based on statistical topic modeling. Then, we design a novel correlative convolutional neural network which uses a shared hidden layer to learn effective representations of the multi-faceted features. The model can discover salient information while being robust to the variations and noises in tweets and sources. To obtain a bird’s-eye view of a crisis event, we further develop an approach to automatically summarize key information of identified tweets. Empirical evaluation on a real Twitter dataset demonstrates the feasibility of discerning relevant tweets for an unseen crisis. The applicability of our proposed approach is further demonstrated with a crisis aider system.

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  1. Source-Aware Crisis-Relevant Tweet Identification and Key Information Summarization

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 19, Issue 3
        Special Section on Advances in Internet-Based Collaborative Technologies
        August 2019
        289 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3329912
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 August 2019
        • Accepted: 1 December 2018
        • Revised: 1 August 2018
        • Received: 1 January 2018
        Published in toit Volume 19, Issue 3

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