[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.
张翼鹏,马敬东. 突发公共卫生事件误导信息受众情感分析及传播特征研究*[J]. 数据分析与知识发现, 2020, 4(12): 45-54.
Zhang Yipeng,Ma Jingdong. Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency. Data Analysis and Knowledge Discovery, 2020, 4(12): 45-54.
Kušen E, Strembeck M . Politics, Sentiments, Misinformation: An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections[J]. Online Social Networks and Media, 2018,5:37-50.
Wang Y X, McKee M, Torbica A , et al. Systematic Literature Review on the Spread of Health-related Misinformation on Social Media[J]. Social Science & Medicine, 2019,240:112552.
Radwan E, Radwan A . The Spread of the Pandemic of Social Media Panic During the COVID-19 Outbreak[J]. European Journal of Environment and Public Health, 2020. DOI: 10.29333/ejeph/8277.
Vicario M D, Quattrociocchi W, Scala A , et al. Polarization and Fake News: Early Warning of Potential Misinformation Targets[J]. ACM Transactions on the Web (TWEB), 2019,13(2):1-22.
Van Kleef G A . The Emerging View of Emotion as Social Information[J]. Social and Personality Psychology Compass, 2010,4(5):331-343.
Bavel J J V, Baicker K, Boggio P S , et al. Using Social and Behavioural Science to Support COVID-19 Pandemic Response[J]. Nature Human Behaviour, 2020,4(5):460-471.
Lewandowsky S, Stritzke W G K, Freund A M , et al. Misinformation, Disinformation, and Violent Conflict: From Iraq and the “War on Terror” to Future Threats to Peace[J]. American Psychologist, 2013,68(7):487-501.
Krause N M, Freiling I, Beets B , et al. Fact-Checking as Risk Communication: The Multi-Layered Risk of Misinformation in Times of COVID-19[J]. Journal of Risk Research, 2020. DOI: 10.1080/13669877.2020.1756385.
Jain S, Sharma V, Kaushal R. Towards Automated Real-Time Detection of Misinformation on Twitter [C]//Proceedings of 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016.
Qazvinian V, Rosengren E, Radev D R, et al. Rumor Has It: Identifying Misinformation in Microblogs [C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2011: 1589-1599.
( Yang Li . Microblog Sentiment Analysis of Jiangsu Environmental Public Service Based on LDA and XGBoost Models[J]. Journal of Nanjing University of Posts and Telecommunications (Social Science), 2019,21(6):23-39.)
Syed-Abdul S, Fernandez-Luque L, Jian W S , et al. Misleading Health-Related Information Promoted Through Video-Based Social Media: Anorexia on YouTube[J]. Journal of Medical Internet Research, 2013,15(2):e30.
Li O Y, Bailey A, Huynh D , et al. YouTube as a Source of Information on COVID-19: A Pandemic of Misinformation?[J]. British Medical Journal Global Health, 2020,5(5):e002604.
Pathak R, Poudel D R, Karmacharya P , et al. YouTube as a Source of Information on Ebola Virus Disease[J]. North American Journal of Medical Sciences, 2015,7(7):306-309.
Sharma M, Yadav K, Yadav N , et al. Zika Virus Pandemic—Analysis of Facebook as a Social Media Health Information Platform[J]. American Journal of Infection Control, 2017,45(3):301-302.
Pennycook G, Mcphetres J, Zhang Y , et al. Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention[J]. Psychological Science, 2020,31(7):770-780.
Pulido C M, Villarejo-Carballido B, Redondo-Sama G , et al. COVID-19 Infodemic: More Retweets for Science-Based Information on Coronavirus than for False Information[J]. International Sociology, 2020,35(4):377-392.
Sharma K, Seo S, Meng C Z , et al. Covid-19 on Social Media: Analyzing Misinformation in Twitter Conversations[OL]. arXiv Preprint, arXiv: 2003. 12309.