Topical semantics of twitter links

Abstract

Twitter, a micro-blogging platform with an estimated 20 million unique monthly visitors and over 100 million registered users, offers an abundance of rich, structured data at a rate exceeding 600 tweets per second. Recent efforts to leverage this social data to rank users by quality and topical relevance have largely focused on the "follow" relationship. Twitter's data offers additional implicit relationships between users, however, such as "retweets" and "mentions". In this paper we investigate the semantics of the follow and retweet relationships. Specifically, we show that the transitivity of topical relevance is better preserved over retweet links, and that retweeting a user is a significantly stronger indicator of topical interest than following him. We demonstrate these properties by ranking users with two variants of the PageRank algorithm; one based on the follows subgraph and one based on the implicit retweet sub-graph. We perform a user study to assess the topical relevance of the resulting top-ranked users. Copyright 2011 ACM.