On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation.

Weichen Wang, Jialing Wu, Subigya Nepal, Alex daSilva, Elin Hedlund, Eilis Murphy, Courtney Rogers, Jeremy Huckins
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引用次数: 3

Abstract

Pandemics significantly impact human daily life. People throughout the world adhere to safety protocols (e.g., social distancing and self-quarantining). As a result, they willingly keep distance from workplace, friends and even family. In such circumstances, in-person social interactions may be substituted with virtual ones via online channels, such as, Instagram and Snapchat. To get insights into this phenomenon, we study a group of undergraduate students before and after the start of COVID-19 pandemic. Specifically, we track N=102 undergraduate students on a small college campus prior to the pandemic using mobile sensing from phones and assign semantic labels to each location they visit on campus where they study, socialize and live. By leveraging their colocation network at these various semantically labeled places on campus, we find that colocations at certain places that possibly proxy higher in-person social interactions (e.g., dormitories, gyms and Greek houses) show significant predictive capability in identifying the individuals' change in social media usage during the pandemic period. We show that we can predict student's change in social media usage during COVID-19 with an F1 score of 0.73 purely from the in-person colocation data generated prior to the pandemic.

社交互动从面对面到在线的转变:基于新冠疫情前校园托管的大学生社交媒体使用变化预测
流行病严重影响人类的日常生活。世界各地的人们都遵守安全协议(例如,保持社交距离和自我隔离)。因此,他们愿意与工作场所、朋友甚至家人保持距离。在这种情况下,面对面的社交互动可能会被通过Instagram和Snapchat等在线渠道进行的虚拟互动所取代。为了深入了解这一现象,我们对新冠肺炎大流行开始前后的一组本科生进行了研究。具体来说,我们在大流行之前使用手机的移动传感跟踪了一所小型大学校园中的N=102名本科生,并为他们在校园里学习、社交和生活的每个地点分配了语义标签。通过利用他们在校园中这些不同语义标记的地方的托管网络,我们发现,在某些地方的托管可能代表更高的面对面社交互动(例如,宿舍、健身房和希腊房屋),在识别个人在大流行期间使用社交媒体的变化方面显示出显着的预测能力。我们表明,我们可以预测学生在COVID-19期间使用社交媒体的变化,F1得分为0.73,仅来自大流行之前生成的现场托管数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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