Users’ interactions with their mobile devices leave behind unique digital footprints that can reveal important information about their characteristics, including their personality. By deploying a wide range of machine learning algorithms and by analyzing patterns of mobile usage data from more than 400 users, this study examines the personality determinants of various mobile usage attributes. Considering the Big-Five personality traits (agreeableness, conscientiousness, extraversion, neuroticism, and openness), correlations between mobile usage attributes and personality traits are presented and discussed. Moreover, the study examines the possibility of predicting users’ personality segments from their mobile usage attributes. Using the K-means clustering algorithm, three distinct personality segments are detected. Subsequently, different machine learning classification models are trained to predict the personality segments of users based on their mobile usage attributes. The results suggest that users’ personality segments can be correctly predicted, with an overall accuracy of 76.17%. The number of contacts on the device is found to be the most significant predictor followed by the frequency and duration of outgoing calls, and then the average time spent on social media applications. Additionally, the study discusses the practical implications of the findings from the perspectives of users, service providers and mobile application providers.

用户与移动设备的交互留下了独特的数字足迹，这些足迹可以揭示有关其特征（包括个性）的重要信息。通过部署广泛的机器学习算法并通过分析来自400多个用户的移动使用数据的模式，本研究检查了各种移动使用属性的个性决定因素。考虑到五种人格特质（愉快，认真，外向，神经质和开放），提出并讨论了移动使用属性和人格特质之间的相关性。此外，研究还研究了根据用户的移动使用属性预测其个性细分的可能性。使用K-均值聚类算法，检测到三个不同的个性段。随后，对不同的机器学习分类模型进行训练，以基于用户的移动使用属性来预测用户的个性细分。结果表明，用户的个性细分可以正确预测，总体准确率为76.17％。发现设备上的联系人数量是最重要的预测指标，其次是拨出电话的频率和持续时间，然后是在社交媒体应用程序上花费的平均时间。此外，该研究从用户，服务提供商和移动应用程序提供商的角度讨论了发现的实际含义。