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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\u2019s-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.<\/jats:p>","DOI":"10.1145\/3300229","type":"journal-article","created":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T12:18:52Z","timestamp":1566994732000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Source-Aware Crisis-Relevant Tweet Identification and Key Information Summarization"],"prefix":"10.1145","volume":"19","author":[{"given":"Xiaodong","family":"Ning","sequence":"first","affiliation":[{"name":"University of New South Wales, Australia"}]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}]},{"given":"Boualem","family":"Benatallah","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}]},{"given":"Yihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Kyoto University"}]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquaire University"}]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}]}],"member":"320","published-online":{"date-parts":[[2019,8,27]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Leveraging the semantics of tweets for adaptive faceted search on twitter. 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