"Birds of a Feather": Does User Homophily Impact Information Diffusion in Social Media?

TL;DR

This paper explores how user homophily influences information spread in social media, using a Bayesian framework to predict diffusion patterns and quantify the impact of attribute similarity on information propagation.

Contribution

It introduces a novel Bayesian approach to quantify the effect of user attribute homophily on information diffusion in social media.

Findings

Homophily significantly affects diffusion prediction accuracy.

Attribute homophily explains 15-25% of diffusion variance.

Choice of homophilous attribute impacts diffusion modeling results.

Abstract

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.