Department of Psychology, Lancaster University.
Department of Psychology of Conflict, Risk and Safety, University of Twente.
Psychol Sci. 2022 Mar;33(3):364-370. doi: 10.1177/09567976211040491. Epub 2022 Feb 17.
Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeated a classic study of intraindividual consistency with secondary data (five data sets) containing 28,692 days of smartphone usage from 780 people. Using per-app measures of pickup frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users ( > 1.46). Random-forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% accuracy for pickup frequency and 38.5% accuracy for duration frequency. This increased to 73.5% and 75.3%, respectively, when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives, and its uniqueness provides both opportunities and risks to privacy.
从数字足迹推断个性的努力一直集中在特质层面的行为稳定性上,而没有考虑情境依赖性。我们使用来自 780 人的二次数据(五个数据集)重复了一项经典的个体内一致性研究,这些数据包含了 28692 天的智能手机使用情况。使用每个应用程序的使用频率和使用时长指标,我们发现,当从同一用户获取日常智能手机使用情况的记录时,用户之间的使用情况记录的一致性明显更高(>1.46)。基于 6 天行为数据训练的随机森林模型以 35.8%的准确率识别出测试数据中的 780 名用户中的每一位,在使用频率上的准确率为 38.5%,在持续时间频率上的准确率为 38.5%。当成功定义为用户出现在前 10 个预测中(即前 1%)时,这一准确率分别提高到 73.5%和 75.3%。因此,我们的数字生活中存在着情境依赖性的行为稳定性,其独特性既为隐私提供了机会,也带来了风险。