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. 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. Understanding homophily can help us build better models for user/item recommendation systems and web personalization services by taking into account users’ similarities and their social behaviors. Existence of demographic homophily, that is homophily based on demographic characteristics, is well established (Feld 1981; Kossinets and Watts 2009). (McPherson, Smith-Lovin, and Cook 2001), for example, cites over a hundred studies that support homophily along multiple dimensions, such as race, ethnicity, sex, gender, age, religion, education, occupation, abilities, beliefs, aspirations, and so on. Less empirical evidence exists for Copyright c © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. homophily in online social networks in which individual interactions are not constrained by geographic and organizational proximity and are instead based on shared interests or expertise. In an online social network of Twitter, for example, one is more likely to find a Semantic Web researcher who is linked to another Semantic Web researcher than to an app developer and vice versa, despite similar demographic characteristics of the two groups of users. One challenge to demonstrate homophily is to define a metric that properly accounts for topical similarity. (Singla and Richardson 2008) used the categories of search queries issued by users, in addition to their demographic characteristics, to measure similarity and demonstrated that people who talk to each other via instant messaging are more likely to be similar than a random pair of users. Others (Weng et al. 2010; Schifanella et al. 2010) found that linked social media users share topical interests and tagging vocabulary, and (Wu et al. 2011) found homophily within categories, with celebrities tending to follow other celebrities, bloggers other bloggers, and so on. In this paper we show homophily on Twitter using Twitter lists as a novel source of evidence for topical similarity. In addition to broadcasting short messages, called tweets, registered Twitter users can follow accounts of other users to receive their tweets. Twitter introduced lists to help users manage the friends they follow. A list is created by some user, referred to as the curator, who names it and adds up to 500 members to it. A curator can create up to 20 lists. Other users can then subscribe to the list to see tweets from list members without having to follow them directly. Essentially, Twitter users categorize others by tagging them with list names. By applying topic modeling techniques to lists, we find the reduced dimension topic space which serves as a basis for measuring similarity between list members. We find that topically similar users are more likely to be linked via a follower relationship than less similar users. In Section “Twitter Lists” we describe our data collection methodology and properties of Twitter lists. Just like tags that are used to annotate resources in social bookmarking sites, list names are used to categorize user accounts on Twitter along multiple dimensions and have a long tailed frequency distribution. Unlike tags, however, lists add a new relational layer to Twitter data, since they are used to indirectly follow users. In Section “Topical Homophily on Twitter”, we use Latent Dirichlet Allocation to learn the topic distribution of Twitter list members in our sample. We define a similarity measure based on these topics and empirically demonstrate existence of homophily. Our work demonstrates the potential of Twitter lists for numerous applications, including discovering communities of shared interests, experts on particular topics, categorizing people within subject matter directories, ranking popular or influential users, recommending interesting users or tweets based on a topic, and so on.