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

Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency

【目的】以新冠肺炎流行期间新浪微博数据为基础,通过文本挖掘的方式获取突发公共卫生事件中的误导信息,揭示误导信息的受众情感特征及其对信息传播的影响。【方法】使用机器学习的方法对相关微博进行分类判别,使用LDA主题模型获取微博相应主题信息,使用词典法对微博相应的评论进行情感极性判别,使用t检验分别对受众情感不同的误导信息微博的评论数、转发数和点赞数进行比较。【结果】样本数据中,误导信息占比为46.28%,误导信息和非误导信息相应微博的评论为负面情感的占比分别为59.32%和54.49%;误导信息中,评论为负面情感的微博的评论数、转发数和点赞数分别比评论为正面情感的微博平均多2.26、2.68和3.29次。【局限】未对非误导信息的传播特征进行研究分析,不排除其与误导信息特征相似的可能;仅选取"新冠肺炎疫情"一个案例,未来需要对更多案例进行横向比较。【结论】突发公共卫生事件中,网络社交媒体中存在相当比重的误导信息。相较于非误导信息,误导信息受众的负向情感比重高。在误导信息中,负面情感的信息相较正面情感的信息转发传播次数多,受众参与程度高。

[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.