Network’s reciprocity: a key determinant of information diffusion over Twitter

The role of social media, particularly Twitter, in ensuring the large-scale propagation of information cannot be overemphasised. This study introduces the recipient network’s reciprocity toward a particular topic as a novel factor that contributes toward a central node’s information propagation potential, in addition to other widely studied factors. It first employs multiple regression analysis to present a model that reveals the prominent roles played by both content popularity, focal ratio, engagement efforts of users, and the recipient network’s reciprocity toward a topic, in determining his or her propagation potential. Further, it investigates the impact of the interaction terms of each of these propagation dimensions and the network’s reciprocity toward the topic on a user’s propagation potential. The results show that the network’s reciprocity toward the topic (i.e. ‘blockchain’ in this study) is important for modelling the diffusion process accurately. Second, applying a multi-methods approach, this study also incorporates fuzzy set qualitative comparative analysis (fsQCA). It reveals four alternative combinations of explanatory variables (propagation dimensions) that are sufficient for achieving the expected outcome (propagation potential of the user/central node). The study found fsQCA results complementing the results of the regression model.