TY - JOUR AU - Melton, Chad A AU - White, Brianna M AU - Davis, Robert L AU - Bednarczyk, Robert A AU - Shaban-Nejad, Arash PY - 2022 DA - 2022/10/17 TI - COVID-19疫苗相关社交媒体数据的微调情绪分析:比较研究JO - J医学互联网Res SP - e40408 VL - 24 IS - 10kw -情感分析KW - DistilRoBERTa KW -自然语言处理KW -社交媒体KW -推特KW - Reddit KW - COVID-19 KW -疫苗KW -疫苗KW -内容分析KW -公共卫生KW -监测KW -错误信息KW -信息病学KW -信息质量AB -背景:新型冠状病毒(COVID-19)的出现和必要的人群隔离导致前所未有数量的新社交媒体用户寻求与大流行相关的信息。目前,全球约有45亿用户,社交媒体数据为近实时分析与疾病爆发和疫苗接种相关的大量文本提供了机会。官员们可以利用这些分析来制定适当的公共卫生信息、数字干预措施、教育材料和政策。目的:我们的研究调查并比较了2020年1月1日至2022年3月1日期间在2个流行社交媒体平台(reddit和twitter)上表达的与COVID-19疫苗相关的公众情绪。方法:为了完成这项任务,我们创建了一个经过微调的蒸馏roberta模型来预测大约950万条推文和7万条Reddit评论的情绪。为了优化我们的模型,我们的团队手动标记了3600条推文的情绪,然后通过反向翻译增强了我们的数据集。然后,使用Python编程语言和拥抱脸情感分析管道,用我们的微调模型对每个社交媒体平台的文本情感进行分类。结果:我们的结果确定,Twitter上表达的平均情绪是消极的(5,215,830/9,518,270,54.8%)多于积极的,Reddit上表达的情绪是积极的(42,316/67,962,62.3%)多于消极的。 Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. Conclusions: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population’s expressed sentiments that facilitate digital literacy, health information–seeking behavior, and precision health promotion could aid in clarifying such misinformation. SN - 1438-8871 UR - //www.mybigtv.com/2022/10/e40408 UR - https://doi.org/10.2196/40408 UR - http://www.ncbi.nlm.nih.gov/pubmed/36174192 DO - 10.2196/40408 ID - info:doi/10.2196/40408 ER -
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