无监督[随机响应]调查机器人检测:寻找高分类准确度的搜索
Carl F Falk1, Amaris Huang1, Michael John Ilagan1
1Department of Psychology, McGill University.
Psychological methods
|March 10, 2025
概括
研究人员可以使用新的无监督算法在在线调查中检测机器人. 使用更多项目,类别和各种项目难度的性能最好,确保数据质量.
科学领域:
- 社会科学 社会科学 社会科学
- 心理测量 心理测量 心理测量
- 数据科学数据科学数据科学
背景情况:
- 在社会科学中,在线调查被广泛使用.
- 机器人的普及威胁到数据质量和研究完整性.
- 当预防措施失败时,检测机器人至关重要.
研究的目的:
- 评估一种新的无监督算法,用于检测调查数据中的机器人.
- 了解影响算法的分类准确度的因素.
- 确定可靠机器人检测的条件.
主要方法:
- 从物品响应理论模型中利用了与假设人类反应的模拟.
- 在35个数据集中用模拟机器人污染了真人数据.
- 采用了一种假设物品可交换性对机器人而不是人类的交换性测试.
主要成果:
- 算法准确性对项目属性,项目数量,潜伏因子和因子相关性敏感.
- 在最佳条件下实现高分类准确度 (约95%+).
- 在某些条件下观察到较低的精度,突出显示性能变化.
结论:
- 无模型,无监督的算法显示出对机器人检测的承诺.
- 通过更多的项目,更多的响应类别和项目难度变化,对分类的准确性得到了提高.
- 研究人员必须考虑这些因素,以确保可靠的机器人检测和维护数据完整性.
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