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ProximityRank: Who are the nearest influencers?

Abstract:

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.