CHASSIS: Conformity Meets Online Information Diffusion

Online information diffusion generates huge volumes of social activities (eg. tweets, retweets posts, comments, likes) among individuals. Existing information diffusion modeling techniques are oblivious to conformity of individuals during the diffusion process, a fundamental human trait according to social psychology theories. Intuitively, conformity captures the extent to which an individual complies with social norms or expectations. In this paper, we present a novel framework called chassis to characterize online information diffusion by bridging classical information diffusion model with conformity from social psychology. To this end, we first extend "Hawkes Process", a well-known statistical technique utilized to model information diffusion, to quantitatively capture two flavors of conformity, informational conformity and normative conformity, hidden in activity sequences. Next, we present a novel semi-parametric inference approach to learn the proposed model. Experimental study with real-world datasets demonstrates the superiority of chassis to state-of-the-art conformity-unaware information diffusion models.