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.
%0 Generic
%1 DeChoudhury2010
%A Choudhury, Munmun De
%A Sundaram, Hari
%A John, Ajita
%A Seligmann, Doree Duncan
%A Kelliher, Aisling
%D 2010
%K homophily impact networks sna twitter
%T Birds of a Feather: Does User Homophily Impact Information Diffusion in Social Media?
%U http://arxiv.org/abs/1006.1702
%X 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.
@misc{DeChoudhury2010,
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.},
added-at = {2011-05-10T19:05:20.000+0200},
author = {Choudhury, Munmun De and Sundaram, Hari and John, Ajita and Seligmann, Doree Duncan and Kelliher, Aisling},
biburl = {https://www.bibsonomy.org/bibtex/2c9764533057b464006669ac6f7b8d24b/enricostano},
interhash = {b7f7e9da42413523e057e1ae67a81163},
intrahash = {c9764533057b464006669ac6f7b8d24b},
keywords = {homophily impact networks sna twitter},
note = {cite arxiv:1006.1702 Comment: 31 pages, 10 figures, 3 tables},
timestamp = {2011-05-10T21:38:45.000+0200},
title = {Birds of a Feather: Does User Homophily Impact Information Diffusion in Social Media?},
url = {http://arxiv.org/abs/1006.1702},
year = 2010
}