The popularity of contradictory information about COVID-19 vaccine on social media in China
作者：
Highlights：
• Content and sentiment analysis, k-medoids clustering for feature extraction according to systematic and heuristic information processing modes.
• Health Belief Models, Planned Behavior Theory for topic, attitude coding.
• Information-feature networks to visualize features' centrality and clustering to detect users' posting patterns.
• Differences in text, display-dimension, topic, sentiment, readability, poster of contradictory information.
• Contextual features impact more on information popularity than content ones.
摘要
•Content and sentiment analysis, k-medoids clustering for feature extraction according to systematic and heuristic information processing modes.•Health Belief Models, Planned Behavior Theory for topic, attitude coding.•Information-feature networks to visualize features' centrality and clustering to detect users' posting patterns.•Differences in text, display-dimension, topic, sentiment, readability, poster of contradictory information.•Contextual features impact more on information popularity than content ones.
论文关键词：COVID-19 vaccine,Weibo,Attitude,Information popularity,Content feature,Contextual feature
论文评审过程：Received 8 January 2022, Revised 1 March 2022, Accepted 1 May 2022, Available online 5 May 2022, Version of Record 9 May 2022.
论文官网地址：https://doi.org/10.1016/j.chb.2022.107320
