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Deciphering Influence on Social Media: A Comparative Analysis of Influential Account Detection Metrics in the Context of Tobacco Promotion
Social Media + Society ( IF 4.636 ) Pub Date : 2024-01-27 , DOI: 10.1177/20563051231224268
Alex Kresovich 1 , Andrew H. Norris 1 , Chandler C. Carter 1 , Yoonsang Kim 1 , Ganna Kostygina 1 , Sherry L. Emery 1
Affiliation  

Influencer marketing spending in the United States was expected to surpass $6 billion in 2023. This marketing tactic poses a public health threat, as research suggests it has been utilized to undercut decades of public health progress—such as gains made against tobacco use among adolescents. Public health and public opinion researchers need practical tools to capture influential accounts on social media. Utilizing X (formerly Twitter) little cigar and cigarillo (LCC) data, we compared seven influential account detection metrics to help clarify our understanding of the functions of existing metrics and the nature of social media discussion of tobacco products. Results indicate that existing influential account detection metrics are non-harmonic and time-sensitive, capturing distinctly different users and categorically different user types. Our results also reveal that these metrics capture distinctly different conversations among influential social media accounts. Our findings suggest that public health and public opinion researchers hoping to conduct analyses of influential social media accounts need to understand each metric’s benefits and limitations and utilize more than one influential account detection metric to increase the likelihood of producing valid and reliable research.

中文翻译:

解读社交媒体的影响力:烟草促销背景下有影响力的账户检测指标的比较分析

预计到 2023 年,美国网红营销支出将超过 60 亿美元。这种营销策略对公共健康构成威胁,因为研究表明,它已被用来削弱数十年的公共卫生进步,例如在青少年吸烟方面取得的成果。公共卫生和舆论研究人员需要实用的工具来捕获社交媒体上有影响力的账户。利用 X(以前称为 Twitter)的小雪茄和小雪茄 (LCC) 数据,我们比较了七个有影响力的帐户检测指标,以帮助澄清我们对现有指标的功能以及烟草产品社交媒体讨论性质的理解。结果表明,现有的有影响力的帐户检测指标是非谐波且时间敏感的,捕获明显不同的用户和明显不同的用户类型。我们的结果还表明,这些指标捕获了有影响力的社交媒体帐户之间截然不同的对话。我们的研究结果表明,希望对有影响力的社交媒体帐户进行分析的公共卫生和舆论研究人员需要了解每个指标的优点和局限性,并利用多个有影响力的帐户检测指标来增加进行有效和可靠研究的可能性。
更新日期:2024-01-27
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