Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact—the effect of real‐world activities on online actions—has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi‐experimental design study that examined the presence of causal relations between real‐world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check‐ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi‐experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions.

中文翻译：

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现实世界活动与社交媒体用户情感表达之间的因果关系

通过在线社交媒体进行的社交互动已成为许多人的日常活动，现实世界（离线）和在线生活已经交织在一起的人数正在持续增长。因此，个人在线和离线活动之间的相互作用已成为众多研究的主题，其中大多数研究了人们在线行为对其离线活动的影响。影响的相反方向（现实活动对在线行为的影响）也受到关注，但程度较小。为了促进后者的影响，本文报告了一项准实验设计研究，该研究研究了在线社交媒体用户的现实活动与他们在线情感表达之间的因果关系。为此，我们从Twitter和Foursquare收集了一个大型数据集（超过17,000个用户），并在两个社交媒体平台上系统地调整了用户内容。用户的Foursquare签入提供了有关其离线活动的信息，而用户的情绪和情绪表达则来自其Twitter帖子。由于我们的研究基于准实验设计，为了最大程度地减少协变量的影响，我们采用了一种创新的倾向得分计算模型。我们的主要发现可以归纳如下：（a）用户的离线活动确实会影响他们的情感表达，包括情感和情绪，这在他们的在线共享文本内容中得到了证明；（b）影响取决于离线活动的类型以及用户是从事还是放弃该活动。