通过减少欺诈数据, 提高在线健康调查的严格性
Wen Zhi Ng1, Sundarimaa Erdembileg1,2, Jean C J Liu3
1Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore, 65 91878576.
Journal of medical Internet research
|August 21, 2025
概括
确保在线健康研究调查的数据完整性至关重要. 这项研究综合了检测和删除欺诈性回复的实用策略, 提高了在线数据收集的严格性.
科学领域:
- 卫生研究方法
- 数字研究中的数据完整性
背景情况:
- 在线调查对于现代健康研究至关重要,为各种人群提供经济高效的数据收集和获取.
- 然而,由于复杂的欺诈性响应 (机器人,重复响应器) 导致数据完整性存在挑战.
研究的目的:
- 提供在线调查中检测和删除欺诈数据的全面综合策略.
- 通过改进数据收集和验证技术,加强在线健康研究的严谨性.
主要方法:
- 集成自动选技术 (例如,CAPTCHA,蜜问题) 和注意力检查 (例如,陷问题).
- 实施强有力的招聘程序 (例如隐藏的资格标准,双阶段选) 和适当的激励机制.
- 检查采样方法 (河流采样,在线小组,众包) 和数据收集后的分析 (元数据,响应模式).
主要成果:
- 建议使用多种策略进行动态欺诈检测和数据过.
- 强调需要不断适应不断发展的欺诈策略,包括人工智能驱动的方法.
- 强调数据收集前,收集期间和收集后战略对于强有力的欺诈检测的重要性.
结论:
- 强有力的数据选策略对于维护在线健康研究的科学完整性至关重要.
- 需要进一步的研究来开发和完善打击日益复杂的欺诈策略的方法.
- 建议采用多方面的适应性方法,以有效过欺诈数据并确保可靠的研究结果.
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