通过援助和培训改善社会机器人检测
Ryan Kenny1, Baruch Fischhoff2, Alex Davis2
1United States Army, Fayetteville, NC, USA.
Human factors
|November 14, 2023
概括
机器人指标得分和培训视频改善了社交机器人检测,而警告没有影响. 这些信息干预有助于用户在线识别欺骗性的社交机器人.
科学领域:
- 人与计算机的互动.
- 社交媒体分析
- 计算社会科学 计算社会科学
背景情况:
- 像Twitter这样的平台上的社交机器人可以促进在线欺骗.
- 有效地检测社交机器人对于减轻错误信息和操纵至关重要.
- 之前的研究已经探索了各种机器人检测方法,但以用户为中心的干预措施需要进一步调查.
研究的目的:
- 评估三种不同的辅助工具在提高个人检测社交机器人的能力方面的有效性.
- 为了比较机器人指标得分,培训视频和警告对用户判断准确性的影响.
- 为评估针对社会机器人脆弱性的干预提供一个实验框架.
主要方法:
- 使用了一个模拟的社交媒体任务,涉及60个Twitter人物.
- 参与者将人物判断为人类或社交机器人,使用机器学习算法估计的机器人概率.
- 两项实验比较了对照组和干预组,这些干预组接受了机器人指标得分,培训视频或警告.
主要成果:
- 机器人指标得分干预显著提高了预测性能,并减少了过度自信.
- 一个培训视频也被证明在改善机器人检测方面是有效的,尽管比分数要少.
- 警告干预没有显著影响参与者检测社交机器人的能力.
结论:
- 信息干预,如机器人评分和培训,可以明显改善社交机器人检测.
- 仅仅向用户警告社交机器人是不足以增强检测能力的.
- 该研究强调了具体干预措施的价值,以减少用户对社交机器人的脆弱性.
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