Measuring Self-Esteem with Passive Sensing.
Measuring Self-Esteem with Passive Sensing.
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DOI：
10.1145/3421937.3421952
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Plötz T
中科院分区：
文献类型：
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作者：
Morshed MB;Saha K;De Choudhury M;Abowd GD;Plötz T
自尊包括个人如何评价自己，是他们成功的重要因素。传统上，自尊是使用基于调查的方法来衡量的。然而，调查受到回顾性回忆和报告偏差等限制，导致需要采取主动的衡量方法。我们的工作使用智能手机传感器来预测自尊，并对大学生进行为期五周的多模式传感研究。我们使用理论驱动的特征（例如电话通信和身体活动）来预测三个维度：表现、社交和外表自尊。我们进行统计建模，包括线性、集成和神经网络回归来衡量自尊。我们最好的模型预测自尊的相关性 (r) 为 0.60，SMAPE 为 7.26%，表明预测准确性很高。我们检查最重要的特征以找到理论上的一致性；例如，社交互动对基于表现和外表的自尊有显着贡献，而身体活动则是对社交自尊最重要的贡献。我们的工作揭示了被动传感器在预测自尊方面的功效，我们将我们的观察结果与文献结合起来，并讨论了我们的工作对定制干预措施和改善福祉的影响。
Self-esteem encompasses how individuals evaluate themselves and is an important contributor to their success. Self-esteem has been traditionally measured using survey-based methodologies. However, surveys suffer from limitations such as retrospective recall and reporting biases, leading to a need for proactive measurement approaches. Our work uses smartphone sensors to predict self-esteem and is situated in a multimodal sensing study on college students for five weeks. We use theory-driven features, such as phone communications and physical activity to predict three dimensions, performance, social, and appearance self-esteem. We conduct statistical modeling including linear, ensemble, and neural network regression to measure self-esteem. Our best model predicts self-esteem with a high correlation (r) of 0.60 and low SMAPE of 7.26% indicating high predictive accuracy. We inspect the top features finding theoretical alignment; for example, social interaction significantly contributes to performance and appearance-based self-esteem, whereas, and physical activity is the most significant contributor towards social self-esteem. Our work reveals the efficacy of passive sensors for predicting self-esteem, and we situate our observations with literature and discuss the implications of our work for tailored interventions and improving wellbeing.