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.
2016 IEEE Latin American Conference on Computational Intelligence (LA CCI)