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, Yu Qing Jolene Teo, Chee Yu Kiew, Bryant Po-Yuen Lim, Yu Liang Lim, 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, Yu Qing Jolene Teo, Chee Yu Kiew, Bryant Po-Yuen Lim, Yu Liang Lim, 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}
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.
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.