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Information Needs Mining of COVID-19 in Chinese Online Health Communities
Big Data Research ( IF 2.673 ) Pub Date : 2021-01-08 , DOI: 10.1016/j.bdr.2021.100193
Jie Wang; Lei Wang; Jing Xu; Yan Peng

This study explores the information needs for the novel coronavirus pneumonia (COVID-19) in Chinese online health communities (OHCs). Based on the question and answer data about COVID-19 in six Chinese OHCs, topic mining and data analysis were conducted. We propose a CL-LDA topic model (Latent Dirichlet Allocation Model with co-occurrence of lexical meaning) based on lexical meaning co-occurrence analysis and LDA topic model. Four main information need topics and their proportion are found in this study, including symptom (45.50%), prevention (36.11%), inspection (10.97%), and treatment (7.42%). We also discover that men are most concerned about symptom information while women are most concerned about prevention information; young users have the largest proportion of information needs, and they are most concerned about prevention information. Experiment results show that the CL-LDA model can well adapt to the topic mining task of short text which is semantic sparse and lacking co-occurrence information in OHCs. The research results are helpful for OHCs to provide accurate information assistance and improve service quality.

更新日期:2021-01-13
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