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. We show 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.

翻译：本研究证明，通过手机传感器收集的活动数据可以可靠地预测人格特质。利用一组可从加速度计记录和运动模式中计算得出的信息丰富的指标，我们在二分类问题上实现了高达0.78的F1分数来预测用户人格。鉴于手机收集的数据量快速增长，我们提出的人格新指标为社会科学的未来研究开辟了令人兴奋的途径。研究结果揭示了不同的行为模式，这些模式对大五人格特质的预测能力存在显著差异。它们有望以前所未有的规模，实现无需问卷的、成本效益高的人格相关问题研究。我们展示了如何将智能手机传感获取的丰富行为数据与机器学习技术相结合，以推动人格研究发展，并帮助实践者和研究者了解人格的不同行为模式。这些发现对于利用手机传感器数据进行人格评估的组织具有实际意义，可指导未来更精确、更高效预测模型的优化。