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Reddit社交媒体平台上关于COVID-19疫苗的公众情感分析和主题建模:呼吁采取行动加强疫苗信心

Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence

Chad A Melton, Olufunto A Olusanya, Nariman Ammar, Arash Shaban-Nejad

摘要:

COVID-19大流行推动了历史上最快速的疫苗开发之一。然而,通过在线社交媒体传播的错误信息往往会导致疫苗的负面情绪和犹豫。为了调查社交媒体中与COVID-19疫苗相关的讨论,我们对2020年12月1日至2021年5月15日期间从13个关注COVID-19疫苗的Reddit社区收集的文本数据进行了情感分析和潜在狄利克雷分配主题建模。数据按月汇总和分析,以检测任何情绪和潜在主题的变化。ty分析表明,这些社区对疫苗相关讨论的积极情绪多于消极情绪,并且随着时间的推移保持不变。主题建模显示,社区成员主要关注副作用,而不是古怪的阴谋论。来自13个subreddit的Covid-19疫苗相关内容显示,这些社区表达的情绪总体上是积极多于消极的,自2020年12月以来没有发生有意义的变化。在整个LDA主题建模中检测到指示疫苗犹豫的关键词。关于疫苗的公众情绪和主题建模分析可以促进实施适当的信息传递、数字干预和新政策,以提高疫苗信心。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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