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  • 标题:Sentiment Analysis of Rumor Spread Amid COVID-19: Based on Weibo Text
  • 本地全文:下载
  • 作者:Peng Wang ; Huimin Shi ; Xiaojie Wu
  • 期刊名称:Healthcare
  • 电子版ISSN:2227-9032
  • 出版年度:2021
  • 卷号:9
  • 期号:10
  • DOI:10.3390/healthcare9101275
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:(1) Background: in early 2020, COVID-19 broke out. Driven by people’s psychology of conformity, panic, group polarization, etc., various rumors appeared and spread wildly, and the Internet became a hotbed of rumors. (2) Methods: the study selected Weibo as the research media, using topic models, time series analysis, sentiment analysis, and Granger causality testing methods to analyze the social media texts related to COVID-19 rumors. (3) Results: in study 1, we obtained 21 topics related to “COVID-19 rumors” and “outbreak rumors” after conducting topic model analysis on Weibo texts; in study 2, we explored the emotional changes of netizens before and after rumor dispelling information was released and found people’s positive emotions first declined and then rose; in study 3, we also explored the emotional changes of netizens before and after the “Wuhan lockdown” event and found positive sentiment of people in non-Wuhan areas increased, while negative sentiment of people in Wuhan increased; in study 4, we studied the relationship between rumor spread and emotional polarity and found negative sentiment and rumor spread was causally interrelated. (4) Conclusion: These findings could help us to intuitively understand the impact of rumors spread on people’s emotions during the COVID-19 pandemic and help the government take measures to reduce panic.
  • 关键词:enCOVID-19;rumors;sentiment analysis;topic model;time series analysis
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