A New Understanding of Friendships in Space: Complex Networks Meet Twitter

摘要

Studies on friendships in online social networks involving geographicdistance have so far relied on the city location provided in users' profiles.Consequently, most of the research on friendships have provided accuracy at thecity level, at best, to designate a user's location. This study analyzes aTwitter dataset because it provides the exact geographic distance betweencorresponding users. We start by introducing a strong definition of "friend" onTwitter (i.e., a definition of bidirectional friendship), requiringbidirectional communication. Next, we utilize geo-tagged mentions delivered byusers to determine their locations, where "@username" is contained anywhere inthe body of tweets. To provide analysis results, we first introduce a friendcounting algorithm. From the fact that Twitter users are likely to postconsecutive tweets in the static mode, we also introduce a two-stage distanceestimation algorithm. As the first of our main contributions, we verify thatthe number of friends of a particular Twitter user follows a well-knownpower-law distribution (i.e., a Zipf's distribution or a Pareto distribution).Our study also provides the following newly-discovered friendship degreerelated to the issue of space: The number of friends according to distancefollows a double power-law (i.e., a double Pareto law) distribution, indicatingthat the probability of befriending a particular Twitter user is significantlyreduced beyond a certain geographic distance between users, termed theseparation point. Our analysis provides concrete evidence that Twitter can be auseful platform for assigning a more accurate scalar value to the degree offriendship between two users.