ProximityRank: Who are the nearest influencers?

Citizens engage in online discussions with more frequency each day producing content relevant locally and globally. Finding influencers, who drive the agenda of such content on Twitter, has become a challenging task. An important factor that boosts the user influence is the geographic proximity with his peers [1]. Based on this finding from previous work, we propose ProximityRank, an extension of the TwitterRank [2] algorithm that brings distance to the equation. ProximityRank exhibits a higher accuracy in ranking users' influence because it takes into account geographic proximity among users, in addition to the similarity of topics in their tweets. Using a dataset of 2.8M tweets, we conduct experiments in different scenarios showing that ProximityRank outperforms previous techniques in the quality of recommendation about whom to follow.

I. Introduction

The widespread use of Twitter as a communication platform let users to publish short messages through several client applications like mobile apps in Android or iOS devices [3] and reach large audiences. The main goal of posting content on Twitter is to reach different groups of people interested in the same topics using a communication the schema built of followers and friends.