Using Lists to Measure Homophily on Twitter

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.