Qirtas Malik Muhammad, Zafeiridi Evi, White Eleanor Bantry, Pesch Dirk
School of Computer Science and Information Technology, University College Cork, T12 K8AF Cork, Ireland.
School of Applied Social Studies, University College Cork, T12 K8AF Cork, Ireland.
Sensors (Basel). 2025 Mar 19;25(6):1903. doi: 10.3390/s25061903.
Loneliness is a global issue which is particularly prevalent among college students, where it poses risks to mental health and academic success. Chronic loneliness can manifest in two primary forms: social loneliness, which is defined by a lack of belonging or a social network, and emotional loneliness, which comes from the absence of deep, meaningful connections. Differentiating between these forms is crucial for designing personalized and targeted interventions. Passive sensing technology offers a promising, unobtrusive approach to detecting loneliness by using behavioural data collected from smartphones and wearables. This study investigates behavioural patterns associated with social and emotional loneliness using passively sensed data from a student population. Our objectives were to (1) identify behavioural patterns linked to social and emotional loneliness, (2) evaluate the predictive power of these patterns for classifying loneliness types, and (3) determine the most significant digital markers used by machine learning models in loneliness prediction. Using statistical analysis, machine learning, and SHAP-based feature importance methods, we identified significant differences in behaviours between socially and Emotionally Lonely students. Specifically, there were distinct differences in phone use and location-based features. Our machine learning analysis shows a strong ability to classify types of loneliness accurately. The XGBoost model achieved the highest accuracy (78.48%) in predicting loneliness. Feature importance analysis found the critical role of phone usage and location-based features in distinguishing between social and emotional loneliness.
孤独是一个全球性问题，在大学生中尤为普遍，它对心理健康和学业成就构成风险。慢性孤独主要有两种表现形式：社交孤独，其定义为缺乏归属感或社交网络；情感孤独，源于缺乏深厚、有意义的人际关系。区分这些形式对于设计个性化和有针对性的干预措施至关重要。被动传感技术提供了一种有前景的、不引人注意的方法，通过使用从智能手机和可穿戴设备收集的行为数据来检测孤独。本研究使用来自学生群体的被动传感数据调查与社交和情感孤独相关的行为模式。我们的目标是：（1）识别与社交和情感孤独相关的行为模式；（2）评估这些模式对孤独类型分类的预测能力；（3）确定机器学习模型在孤独预测中使用的最重要数字标记。通过统计分析、机器学习和基于SHAP的特征重要性方法，我们确定了社交孤独和情感孤独学生在行为上的显著差异。具体而言，在手机使用和基于位置的特征方面存在明显差异。我们的机器学习分析显示出准确分类孤独类型的强大能力。XGBoost模型在预测孤独方面达到了最高准确率（78.48%）。特征重要性分析发现手机使用和基于位置的特征在区分社交孤独和情感孤独方面的关键作用。
