[Objective] This paper examines mis-information on public health emergency (i.e., the COVID-19 epidemic), aiming to reveal the public’s sentiments on mis-information and the latter’s dissemination patterns. [Methods] We retrieved our data from Sina Weibo and categorized the relevant microblog posts using machine learning techniques. Then, we extracted the post topics with LDA model and decided the emotional polarity of comments using dictionary method. Finally, we used T-test to compare the number of comments, shares and likes received by mis-information posts with different sentiments. [Results] We found that 46.28% of the retrieved blogs had mis-information. 59.32% of the posts with mis-information and 54.49% of the posts with accurate information yielded negative emotion among readers. On average, the misinformation posts with negative sentiments received more comments, shares and likes than those with positive sentiments (2.26, 2.68 and 3.29). [Limitations] We only examined COVID-19 related posts and did not investigate the dissemination of accurate information. [Conclusions] Public health emergency generates much mis-information. The sentiments of misinformation readers are more negative than those of normal information. Weibo posts with misinformation and negative sentiments attract more online participation.
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