Social Media Recruitment in Indigenous and Native American Populations: Challenges in the AI Age.

IF 3.9 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Nadia Diamond-Smith, Alison Comfort, Anna Epperson, Alicia R Riley, Natalie Beylin, Mary Garcia, Sarah Francis, Lucía Abascal Miguel
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引用次数: 0

Abstract

Unlabelled: Using social media recruitment for public health research presents both opportunities and challenges. Despite its increased use, few studies have detailed the practical issues, challenges encountered, and alternative strategies available for social media recruitment. This paper explores strategies for recruiting Indigenous and Native American populations in California for a study on COVID-19 vaccination and social networks. We describe different recruitment approaches, challenges faced, and pros and cons of strategies used to enhance data quality and efficiency, including survey design considerations, Facebook targeting versus use of research panels, quality assurance checks, and decisions around participant incentives. Our local setting involved recruiting Native American and Mesoamerican Indigenous individuals living in California through social media platforms. We highlight key adaptations to survey design, recruitment strategies, and data cleaning processes, noting what approaches that were effective and those that were not. Despite targeted efforts and community collaboration, recruitment was limited, and fraudulent data from bots significantly compromised data quality. Standard Facebook targeting approaches were largely unsuccessful. Our findings suggest that the increasing sophistication of artificial intelligence is becoming a substantial obstacle to authentic participant recruitment through social media. We offer recommendations to improve recruitment of hard-to-reach populations and mitigate AI-related fraud risks in future research.

土著和美洲原住民群体的社交媒体招聘:人工智能时代的挑战。
未标记:利用社交媒体招募公共卫生研究人员既带来机遇,也带来挑战。尽管社交媒体的使用越来越多,但很少有研究详细说明社交媒体招聘的实际问题、遇到的挑战以及可用的替代策略。本文探讨了在加利福尼亚州招募土著和美洲原住民进行COVID-19疫苗接种和社交网络研究的策略。我们描述了不同的招聘方法、面临的挑战,以及用于提高数据质量和效率的策略的利弊,包括调查设计考虑因素、Facebook目标与研究小组的使用、质量保证检查以及围绕参与者激励的决策。我们的当地环境包括通过社交媒体平台招募居住在加州的美洲原住民和中美洲原住民。我们强调了对调查设计、招聘策略和数据清理流程的关键调整,指出了哪些方法是有效的,哪些是无效的。尽管有针对性的努力和社区合作,但招聘有限,来自机器人的欺诈性数据严重影响了数据质量。标准的Facebook定位方法在很大程度上是不成功的。我们的研究结果表明,越来越复杂的人工智能正在成为通过社交媒体招募真实参与者的重大障碍。在未来的研究中,我们提出了一些建议,以改善对难以接触到的人群的招募,并减轻与人工智能相关的欺诈风险。
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来源期刊
CiteScore
13.70
自引率
2.40%
发文量
136
审稿时长
12 weeks
期刊介绍: JMIR Public Health & Surveillance (JPHS) is a renowned scholarly journal indexed on PubMed. It follows a rigorous peer-review process and covers a wide range of disciplines. The journal distinguishes itself by its unique focus on the intersection of technology and innovation in the field of public health. JPHS delves into diverse topics such as public health informatics, surveillance systems, rapid reports, participatory epidemiology, infodemiology, infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media and social media campaigns, health communication, and emerging population health analysis systems and tools.
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