Abstract

Homophily is the tendency of individuals in a social system to link to others who are similar to them and understanding homophily can help us build better user models for personalization and recommender systems. Many studies have verified homophily along demographic dimensions, such as age, location, occupation, etc., not only in real-world social networks but also online. However, there is limited research showing that homophily also exists when similarity is judged by topics of expertise or interests. We demonstrate the existence of topical homophily on Twitter using a novel source of evidence provided by Twitter lists. In this paper, we use LDA to extract topics from Twitter lists (a collection of user accounts created by some user that others can follow) and measure similarity between listed users based on the learned topics. We show that topically similar users are more likely to be linked via a follow relationship than less similar users.
Homophily is a strong organizing principle of social systems and has been used to explain human and social behavior. Homophily refers to the tendency of individuals in a social system to link with others who are similar to them rather than those who are less similar. The community structure homophily imposes on the social network may, in turn, through the processes of influence (Christakis and Fowler 2007) and selection (Crandall et al. 2008) cause linked individuals to become even more similar. Over time, preferential linking will structure the network in such a way as to make the behavior of individuals (Lerman et al. 2011) and even future friendships (Liben-Nowell and Kleinberg 2007) more predictable.

Date

January 1, 1970

Authors

Jeon Hyung Kang, Kristina Lerman

Journal

AAAI workshop on Intelligent techniques for web personalization and recommendation

Volume

18