使用推文中发现的新冠肺炎疫苗态度预测传统调查中的疫苗认知:推文的信息学研究。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2023-11-30 DOI:10.2196/43700
Nekabari Sigalo, Vanessa Frias-Martinez
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引用次数: 0

摘要

背景:传统上,调查是为了回答与公共卫生有关的问题,但执行成本可能很高。然而,研究人员旨在从调查中提取的信息可能会从社交媒体中检索出来,这些数据访问性很高,收集成本也较低。目的:在本研究中,我们评估从家庭脉搏调查中收集的对新冠肺炎疫苗的态度是否可以使用从推特中提取的态度进行预测。最终,我们想确定推特是否能为我们提供与传统调查中观察到的类似的信息,或者,省钱是否是以失去丰富数据为代价的。方法:从2021年1月6日至5月25日收集的家庭脉搏调查(HPS)中提取新冠肺炎疫苗态度。在同一时期,推特的流媒体API用于收集新冠肺炎疫苗推文。对推特进行情绪和情绪分析,以检查推特上对新冠肺炎疫苗的态度。进行了广义线性模型(GLM)和广义线性混合模型(GLMM),以评估推特上新冠肺炎疫苗态度预测HPS中疫苗态度的能力。结果:根据模型,GLM和GLMM显示出(1)符合疫苗的HPS受访者的百分比与表达积极情绪和信任的推文的百分比之间的显著关系;以及介于(2)对疫苗犹豫不决的HPS受访者的百分比和表达负面情绪的推文的百分比之间。在GLM和GLMMS的调查中,推特上表达的积极看法在预测积极看法方面表现良好;而推特上表达的负面看法在预测调查中的负面看法方面表现良好,但仅适用于GLM。结论:这些发现表明,研究人员旨在从调查中提取的信息也可能从更容易访问的数据源中检索,如推特数据。利用推特数据和传统调查,可以更全面、更细致地了解新冠肺炎疫苗认知,促进循证决策和量身定制的公共卫生战略。临床试验:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study.

Background: Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect.

Objective: This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data.

Methods: COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS.

Results: The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey.

Conclusions: These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies.

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