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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.

This study aims to estimate the influence of offline activity on users’ online behavior, relying on a matching method to reduce the effect of confounding variables. We analyze activities of 850 users who are active on both Twitter and Foursquare social networks. Users’ offline activity is extracted from Foursquare posts and users’ online behavior is extracted from Twitter posts. Users’ interests, representing their online behavior, are extracted with regards to a set of topics in several subsequent time intervals. The shift of users’ interests across different time intervals is taken as a measure of user behavior change on the social network. On the other hand, we employ user check-ins at a gym or fitness center as a sign of exercise and consider it to be an offline activity. In order to find the effect of exercise on online behavior, we identify users who did not go to the gym for at least two months but did so at least nine times in the next three months. We show that shift in interest reduces significantly for users after they start exercising, which implies that the offline activity of exercising can influence how users’ interests are shaped and change on the social network over time.

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