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• W4283457777 abstract "Social media data such as tweets have been seen as a convenient source of information to enhance situational awareness, and to assist local governments in decision making and response actions in crisis. However, extracting the relevant information for different types of situational awareness has been challenging. Existing studies have investigated classifications of crisis information on social media, but not much focus has been put on the classification of pandemic related information. Pandemics are public health related crisis that present unique characteristics. We propose to classify pandemic tweets from three perspectives, i.e., informativeness, geographic view, and information source, after a comprehensive analysis of the factors determining the relevance of information to situational awareness. The joint use of three-faceted classifications will enable the identification of relevant data for multiple purposes of situational awareness. We manually annotate a dataset with COVID-19 tweets and explore multi-task learning models for the classification of three tasks simultaneously. The proposed multi-task neural network models show improved performance compared with single learning models. We also find that pretraining multi-task models with relevant crisis datasets can further boost the performance. Specifically, multi-task models can significantly increase the recall of ‘informative’ and ‘local’ tweets, which are important for local response actions and policy decision making." @default.
• W4283457777 created "2022-06-26" @default.
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• W4283457777 date "2022-06-26" @default.
• W4283457777 modified "2023-10-01" @default.
• W4283457777 title "Multi-task Models for Multi-faceted Classification of Pandemic Information on Social Media" @default.
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• W4283457777 doi "https://doi.org/10.1145/3501247.3531552" @default.
• W4283457777 hasPublicationYear "2022" @default.
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