This study provides evidence that personality can be reliably predicted from
activity data collected through mobile phone sensors. Employing a set of well
informed indicators calculable from accelerometer records and movement
patterns, we were able to predict users' personality up to a 0.78 F1 score on a
two class problem. Given the fast growing number of data collected from mobile
phones, our novel personality indicators open the door to exciting avenues for
future research in social sciences. Our results reveal distinct behavioral
patterns that proved to be differentially predictive of big five personality
traits. They potentially enable cost effective, questionnaire free
investigation of personality related questions at an unprecedented scale.
Overall, this paper shows how a combination of rich behavioral data obtained
with smartphone sensing and the use of machine learning techniques can help to
advance personality research and can inform both practitioners and researchers
about the different behavioral patterns of personality. These findings have
practical implications for organizations harnessing mobile sensor data for
personality assessment, guiding the refinement of more precise and efficient
prediction models in the future.

通过手机传感器收集的活动数据可可靠地预测人的个性。使用从加速度计记录和运动模式计算出的指标，我们能够在两类问题上预测用户的个性，F1 得分最高可达 0.78。这些新颖的个性指标为社会科学的未来研究开辟了新的途径。我们的结果揭示了不同的行为模式，这些模式对五个大的人格特征有不同的预测作用。它们为无问卷调查地研究与个性相关的问题提供了成本效益高、前所未有规模的可能性。总体而言，本文展示了通过智能手机传感和机器学习技术获取丰富的行为数据如何促进个性研究，并能够为从业者和研究人员提供有关个性不同行为模式的信息。这些发现对于利用移动传感器数据进行个性评估的组织具有实际意义，并将指导未来更精确和高效的预测模型的改进。