  This article investigates the impact of user homophily on the social process
of information diffusion in online social media. Over several decades, social
scientists have been interested in the idea that similarity breeds connection:
precisely known as "homophily". Homophily has been extensively studied in the
social sciences and refers to the idea that users in a social system tend to
bond more with ones who are similar to them than to ones who are dissimilar.
The key observation is that homophily structures the ego-networks of
individuals and impacts their communication behavior. It is therefore likely to
effect the mechanisms in which information propagates among them. To this
effect, we investigate the interplay between homophily along diverse user
attributes and the information diffusion process on social media. In our
approach, we first extract diffusion characteristics---corresponding to the
baseline social graph as well as graphs filtered on different user attributes
(e.g. location, activity). Second, we propose a Dynamic Bayesian Network based
framework to predict diffusion characteristics at a future time. Third, the
impact of attribute homophily is quantified by the ability of the predicted
characteristics in explaining actual diffusion, and external variables,
including trends in search and news. Experimental results on a large Twitter
dataset demonstrate that choice of the homophilous attribute can impact the
prediction of information diffusion, given a specific metric and a topic. In
most cases, attribute homophily is able to explain the actual diffusion and
external trends by ~15-25% over cases when homophily is not considered.
