The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. ty analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.
翻译:通过在线社交媒体传播的错误信息往往导致负面的疫苗情绪和犹豫不决。为了调查社交媒体上与COVID-19疫苗有关的讨论,我们进行了情绪分析,并以13个Reddit社区收集的文本数据为模型,以2020年12月1日到2021年5月15日的COVID-19疫苗为重点,进行了冷淡的Drichlet分配;对数据进行了逐月汇总和分析,以发现任何情绪和潜在议题的变化。ty分析表明,这些社区对与疫苗有关的讨论表示的积极情绪多于消极情绪,并且一直停滞不前。对披露的社区成员进行专题模拟,主要侧重于副作用,而不是超自然的阴谋理论。Covid-1913次编辑的与疫苗有关的内容表明,这些社区表达的情绪总体上是积极的,而不是消极的,而且自2020年12月以来没有发生有意义的变化。在整个LDAD的模型专题中都发现了疫苗免疫问题的关键词。关于疫苗的建模分析有助于执行适当的信息、数字干预和新的政策。