Liu Tony, Nicholas Jennifer, Theilig Max M, Guntuku Sharath C, Kording Konrad, Mohr David C, Ungar Lyle
University of Pennsylvania, USA.
Northwestern University, USA.
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2019 Dec;3(4). doi: 10.1145/3369820.
Estimating the category and quality of interpersonal relationships from ubiquitous phone sensor data matters for studying mental well-being and social support. Prior work focused on using communication volume to estimate broad relationship categories, often with small samples. Here we contextualize communications by combining phone logs with demographic and location data to predict interpersonal relationship roles on a varied sample population using automated machine learning methods, producing better performance (1 = 0.68) than using communication features alone (1 = 0.62). We also explore the effect of age variation in the underlying training sample on interpersonal relationship prediction and find that models trained on younger subgroups, which is popular in the field via student participation and recruitment, generalize poorly to the wider population. Our results not only illustrate the value of using data across demographics, communication patterns and semantic locations for relationship prediction, but also underscore the importance of considering population heterogeneity in phone-based personal sensing studies.
从无处不在的手机传感器数据中估计人际关系的类别和质量对于研究心理健康和社会支持至关重要。先前的工作侧重于使用通信量来估计广泛的关系类别，样本通常较小。在这里，我们通过将电话记录与人口统计和位置数据相结合来对通信进行情境化，以使用自动化机器学习方法在不同的样本人口中预测人际关系角色，其性能（F1 = 0.68）优于仅使用通信特征（F1 = 0.62）。我们还探讨了基础训练样本中的年龄差异对人际关系预测的影响，发现通过学生参与和招募在该领域很流行的、基于较年轻亚组训练的模型，对更广泛人群的泛化能力较差。我们的结果不仅说明了使用跨人口统计、通信模式和语义位置的数据进行关系预测的价值，还强调了在基于手机的个人传感研究中考虑人群异质性的重要性。
