Social Interaction Data-sets in the Age of Covid-19: a Case Study on Digital Commensality

Eleonora Ceccaldi, Gabriele De Lucia, Radoslaw Niewiadomski, G. Volpe, M. Mancini
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引用次数: 1

Abstract

Research focusing on social interaction often leverages data-sets, allowing annotation, analysis, and modeling of social behavior. When it comes to commensality, researchers have started working on computational models of food and eating-related activities recognition. The growing research area known as Digital Commensality, has focused on meals shared online, for instance, through videochat. However, to investigate this topic, traditional data-sets recorded in laboratory settings may not be the best option in terms of ecological validity. Covid-19 restrictions and lock-downs have increased in online gatherings, with many people becoming used to the idea of sharing meals online. Following this trend, we propose the concept of collecting data by recording online interactions and discuss the challenges related to this methodology. We illustrate our approach in creating the first Digital Commensality data-set, containing recordings of food-related social interactions collected online during the Covid-19 outbreak.
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新冠肺炎时代的社会互动数据集:以数字共生为例
关注社会互动的研究经常利用数据集,允许对社会行为进行注释、分析和建模。在共栖性方面,研究人员已经开始研究食物和饮食相关活动识别的计算模型。被称为“数字共栖”(Digital Commensality)的新兴研究领域主要关注通过视频聊天等方式在网上分享的食物。然而,为了研究这一主题,在实验室环境中记录的传统数据集可能不是生态有效性的最佳选择。新冠肺炎疫情对网络聚会的限制和封锁有所增加,许多人已经习惯了在网上分享食物的想法。根据这一趋势,我们提出了通过记录在线互动来收集数据的概念,并讨论了与此方法相关的挑战。我们展示了创建首个数字共栖数据集的方法,该数据集包含在Covid-19疫情期间在线收集的与食物相关的社交互动记录。
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