About Us
Ranking people, places, and things according to their fame, quality, or significance is an important task, serving to direct greater attention to prominent entities at the expense of lesser ones. Top 10 (or 100) lists satisfy people's need for order, and their curiosity about other people's opinions. Rank orderings are by nature time-dependent, subjective, and culturally biased. Still, we study the problem of ranking entities (primarily people) by "significance" through algorithmic methods.
We exploit a variety of data sources (including news frequency, web hits, Wikipedia content and structure) to develop factor analysis-based methods that rank-order the fame and significance of over 800,000 people appearing within Wikipedia. We validate the performance of our measures against expert-generated ranking lists of historical, sports, and entertainment figures. We build on our modeling to study several issues of cultural significance: what biases govern canonization in a reference source like Wikipedia, and which articles are longer or shorter than merited. Despite the online encyclopedia's desire to obtain objectivity, we discover interesting biases in Wikipedia's coverage across different ethnic and gender groups.