Finding Trendsetters on Yelp Dataset

Abstract

The search for Trendsetters in social networks turned to be a complex research topic that has gained much attention. The work here presented uses big data analytics to find who better spreads the word in a social network and is innovative in their choices. The analysis on the Yelp platform can be divided in three parts: first, we justify the use of Tips frequency as a variable to profile business popularity. Second we analyze Tips frequency to select businesses that fit a growing popularity profile. And third we graph mine the sociographs generated by the users that interacted with each selected business. Top nodes are ranked by using Indegree, Eigenvector centrality, Pagerank and a Trendsetter algorithms, and we compare the relative performance of each algorithm. Our findings indicate that the Trendsetter ranking algorithm is the most performant at finding nodes that best reflect the Trendsetter properties.