The reflection of offline activities on users' online social behavior: An observational study

The ever increasing presence of online social networks in users' daily lives has led to the interplay between users' online and offline activities. There have already been several works that have studied the impact of users' online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users' offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users' socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users' offline behavior that affects the level of users' exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users' online topical interests and sentiment; and (2) the causal relations between users' socially situated offline activities and their online social behavior can be used to build effective predictive models of users' online topical interests and sentiments.