Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand.

中文翻译：

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检查围绕麻疹疫苗接种的普遍负面情绪：2017 年至 2022 年 Twitter 帖子的无监督深度学习。

尽管麻疹疫苗的安全性和临床功效已得到证实，但许多国家对疫苗的犹豫或拒绝达到了新的高度，并因此经历了麻疹感染的死灰复燃。通过使用新颖的机器学习工具，我们通过分析 5 年来的公共 Twitter 帖子，调查了与麻疹疫苗接种相关的普遍负面情绪。我们使用与“麻疹”和“疫苗”相关的搜索词提取原始推文，并从 2017 年 1 月 1 日到 2022 年 12 月 15 日以英文发布。其中，155,363 条推文被识别为来自独特个体的负面情绪推文，通过使用来自 Transformers (BERT) 命名实体识别的双向编码器表示和 SieBERT（英语分析模型中的预训练情感）。随后由研究调查人员进行主题建模和定性主题分析。应用BERTopic后总共生成了11个主题。为了促进对结果的全球讨论，通过迭代主题分析将主题分为四个不同的主题。其中包括 (a) 拒绝“反疫苗者”或反疫苗情绪，(b) 关于麻疹疫苗接种的误解和错误信息，(c) 由于 COVID-19 相关政策而产生的负移情，以及 (d) 公众对当代麻疹疫情的反应。主题 1 强调当前的公共话语可能会因经常使用贬低性语言而进一步疏远那些对疫苗犹豫不决的人，而主题 2 和 3 则强调与麻疹疫苗接种相关的负面情绪和否认心理倾向背后的误解和错误信息的类型。偏见。尽管如此，该分析仅基于 Twitter，并且仅包含英文推文；因此，研究结果不一定适用于非西方社区。重要的是要进一步了解那些对疫苗犹豫不决的人的想法和感受，以解决当前的问题。