Source-aware crisis-relevant tweet identification and key information summarization

Xiaodong Ning, Lina Yao, Boualem Benatallah, Yihong Zhang, Quan Z. Sheng, Salil S. Kanhere

Research output: Contribution to journalArticle

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. Onemajor 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.

Original languageEnglish
Article number37
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Internet Technology
Volume19
Issue number3
DOIs
Publication statusPublished - Nov 2019

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Keywords

  • Convolutional neural network
  • Information summarization
  • Social media

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