Recent years have witnessed the prevalence and use of social media during crises, such as Twitter, which has been becoming a valuable information source for offering better responses to crisis and emergency situations by the authorities. However, the sheer amount of information of tweets can't be directly used. In such context, distinguishing the most important and informative tweets is crucial to enhance emergency situation awareness. In this paper, we design a convolutional neural network based model to automatically detect crisis-related tweets. We explore the twitter-specific linguistic, sentimental and emotional analysis along with statistical topic modeling to identify a set of quality features. We then incorporate them to into a convolutional neural network model to identify crisis-related tweets. Experiments on real-world Twitter dataset demonstrate the effectiveness of our proposed model.
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