社交媒体在人类行为和幸福感纵向研究中的被动传感器

Koustuv Saha, A. Bayraktaroglu, A. Campbell, N. Chawla, M. Choudhury, S. D’Mello, A. Dey, Ge Gao, Julie M. Gregg, Krithika Jagannath, G. Mark, Gonzalo J. Martínez, Stephen M. Mattingly, E. Moskal, Anusha Sirigiri, A. Striegel, Dong Whi Yoo
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引用次数: 39

摘要

社交媒体是一个分享想法和与他人联系的平台。社交媒体的普遍使用也使研究人员能够研究人类行为,因为数据可以以一种廉价且不引人注目的方式收集。社交媒体不仅提供了一种被动的方式来大规模收集历史数据,它还起到了“语言”传感器的作用,提供了关于个人社会生态背景的丰富信号。本案例研究介绍了一个基础设施框架,以说明在正在进行的工作场所绩效多模态感知研究(N=757)的背景下,大规模被动收集社交媒体数据的可行性。我们研究了我们的数据集与人口统计、个性和个人幸福属性的关系。重要的是,作为研究选择偏差的一种手段,我们研究了选择同意社交媒体数据共享的个人与不同意社交媒体数据共享的个人的特征。我们的工作为HCI领域的研究提供了实践经验和启示,这些研究试图进行类似的纵向研究,以利用社交媒体数据的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior and Wellbeing
Social media serves as a platform to share thoughts and connect with others. The ubiquitous use of social media also enables researchers to study human behavior as the data can be collected in an inexpensive and unobtrusive way. Not only does social media provide a passive means to collect historical data at scale, it also functions as a "verbal" sensor, providing rich signals about an individual's social ecological context. This case study introduces an infrastructural framework to illustrate the feasibility of passively collecting social media data at scale in the context of an ongoing multimodal sensing study of workplace performance (N=757). We study our dataset in its relationship with demographic, personality, and wellbeing attributes of individuals. Importantly, as a means to study selection bias, we examine what characterizes individuals who choose to consent to social media data sharing vs. those who do not. Our work provides practical experiences and implications for research in the HCI field who seek to conduct similar longitudinal studies that harness the potential of social media data.
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