研究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。

IF 4.2 2区 心理学 Q1 PSYCHOLOGY, SOCIAL
Qin Xiang Ng, Yu Qing Jolene Teo, Chee Yu Kiew, Bryant Po-Yuen Lim, Yu Liang Lim, Tau Ming Liew
{"title":"研究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。","authors":"Qin Xiang Ng,&nbsp;Yu Qing Jolene Teo,&nbsp;Chee Yu Kiew,&nbsp;Bryant Po-Yuen Lim,&nbsp;Yu Liang Lim,&nbsp;Tau Ming Liew","doi":"10.1089/cyber.2023.0025","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10872,"journal":{"name":"Cyberpsychology, behavior and social networking","volume":"26 8","pages":"621-630"},"PeriodicalIF":4.2000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022.\",\"authors\":\"Qin Xiang Ng,&nbsp;Yu Qing Jolene Teo,&nbsp;Chee Yu Kiew,&nbsp;Bryant Po-Yuen Lim,&nbsp;Yu Liang Lim,&nbsp;Tau Ming Liew\",\"doi\":\"10.1089/cyber.2023.0025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":10872,\"journal\":{\"name\":\"Cyberpsychology, behavior and social networking\",\"volume\":\"26 8\",\"pages\":\"621-630\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cyberpsychology, behavior and social networking\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1089/cyber.2023.0025\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, SOCIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cyberpsychology, behavior and social networking","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1089/cyber.2023.0025","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, SOCIAL","Score":null,"Total":0}
引用次数: 0

摘要

尽管麻疹疫苗的安全性和临床有效性已得到证实,但许多国家对疫苗的犹豫或拒绝达到了新的高度,因此麻疹感染正在重新出现。通过使用新型机器学习工具,我们通过分析5年期间的公共推特帖子,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,并在2017年1月1日至2022年12月15日期间发布英文推文。其中,通过使用变形金刚(BERT)命名实体识别的双向编码器表示和SieBERT(一种预训练的英语情感分析模型),155,363条推文被识别为来自独特个体的负面情绪推文。随后由研究人员进行主题建模和定性主题分析。应用BERTopic后共生成了11个主题。为了促进对结果的全球讨论,通过反复的主题分析,将这些主题分为四个不同的主题。这些因素包括(a)拒绝“反疫苗者”或反疫苗情绪,(b)对麻疹疫苗接种的误解和错误信息,(c)与COVID-19相关政策造成的负面转移,以及(d)公众对当代麻疹疫情的反应。主题1强调,由于经常使用贬低性的语言,目前的公共话语可能进一步疏远那些对疫苗犹豫不决的人,而主题2和主题3强调了误解和错误信息的类型,这些误解和错误信息是与麻疹疫苗接种有关的负面情绪和不确认偏见的心理倾向的基础。尽管如此,该分析仅基于Twitter,且仅包括英文推文;因此,研究结果不一定适用于非西方社会。重要的是要进一步了解那些对解决手头问题犹豫不决的人的想法和感受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
9.60
自引率
3.00%
发文量
123
期刊介绍: Cyberpsychology, Behavior, and Social Networking is a leading peer-reviewed journal that is recognized for its authoritative research on the social, behavioral, and psychological impacts of contemporary social networking practices. The journal covers a wide range of platforms, including Twitter, Facebook, internet gaming, and e-commerce, and examines how these digital environments shape human interaction and societal norms. For over two decades, this journal has been a pioneering voice in the exploration of social networking and virtual reality, establishing itself as an indispensable resource for professionals and academics in the field. It is particularly celebrated for its swift dissemination of findings through rapid communication articles, alongside comprehensive, in-depth studies that delve into the multifaceted effects of interactive technologies on both individual behavior and broader societal trends. The journal's scope encompasses the full spectrum of impacts—highlighting not only the potential benefits but also the challenges that arise as a result of these technologies. By providing a platform for rigorous research and critical discussions, it fosters a deeper understanding of the complex interplay between technology and human behavior.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信