背景:疫苗是至关重要的公共卫生工具,尽管疫苗的犹豫继续对疫苗的全面摄取构成重大威胁,因此,社区健康。了解和跟踪疫苗犹豫对于有效的公共卫生干预措施至关重要;然而,传统的调查方法存在各种局限性。
目的:本研究旨在创建一个实时,基于自然语言处理(NLP)的工具,用于评估3个著名社交媒体平台上的疫苗情绪和犹豫。
方法:我们从Twitter(随后更名为X)挖掘并策划了英语讨论,Reddit,和YouTube社交媒体平台在2011年1月1日至2021年10月31日之间发布,涉及人乳头瘤病毒;麻疹,腮腺炎,风疹和未指明的疫苗。我们测试了多种NLP算法,将疫苗情绪分类为阳性,中性,或阴性,并使用世界卫生组织(WHO)3Cs对疫苗犹豫进行分类(置信度,自满,和便利性)犹豫模型,将在线仪表板概念化,以说明和说明趋势。
结果:我们收集了超过8600万次讨论。我们表现最好的NLP模型显示,情感分类的准确度从0.51到0.78,犹豫分类的准确度从0.69到0.91。我们平台上的探索性分析强调了在线活动中关于疫苗情绪和犹豫的变化,为不同的疫苗提供独特的模式。
结论:我们的创新系统对主要社交网络中的3个疫苗主题进行情绪和犹豫的实时分析,提供关键的趋势见解,以协助旨在提高疫苗使用率和公共卫生的运动。
BACKGROUND: Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.
OBJECTIVE: This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms.
METHODS: We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization\'s (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends.
RESULTS: We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines.
CONCLUSIONS: Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.