In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer‐based predictions from digital footprints can complement—and in some circumstances even replace—traditional self‐report measures, which suffer from well‐known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction‐relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so‐called personality nuances—item‐level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out‐of‐sample cross‐validation. In nine out of 10 cases across the two modelling approaches, nuance‐based models improved the prediction of personality over the trait‐based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology

中文翻译：

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定位项目级别的细微差别会导致数字足迹对个性预测的微小但稳健的改善

在过去的十年中，研究人员证明，可以从数字足迹数据（包括Facebook的喜欢，推文，博客文章，图片和交易记录）准确预测个性。这种基于数字足迹的基于计算机的预测可以补充（在某些情况下甚至可以替代）传统的自报告度量，这些度量遭受众所周知的响应偏差并且难以扩展。但是，这些先前的研究集中在总性状评分（即一个人的外向性评分）的预测上，这可能使五大特质下人格等级的理论水平上与预测相关的信息模糊不清。特别，新的研究表明，通过所谓的人格细微差别（人格的项目级表示）可以更好地表示人格，并且利用这些细微差别可以改善预测性能。本工作检验了以下假设：可以通过首先预测人格差异并随后汇总为分数，而不是直接预测特征分数，来改善数字足迹数据中的个性预测。为了检验这一假设，我们采用最小绝对收缩和选择算子回归以及随机森林模型，使用样本外交叉验证来预测物品和性状。在这两种建模方法中，十分之九的情况下，基于细微差别的模型比基于特征的方法对人格的预测提高了很小但有意义的程度（4.25％或1.）。平均69％（取决于方法）。讨论了对人格预测和人格差异的暗示。©2020欧洲人格心理学协会