  In this work, we present the Klout Score, an influence scoring system that
assigns scores to 750 million users across 9 different social networks on a
daily basis. We propose a hierarchical framework for generating an influence
score for each user, by incorporating information for the user from multiple
networks and communities. Over 3600 features that capture signals of
influential interactions are aggregated across multiple dimensions for each
user. The features are scalably generated by processing over 45 billion
interactions from social networks every day, as well as by incorporating
factors that indicate real world influence. Supervised models trained from
labeled data determine the weights for features, and the final Klout Score is
obtained by hierarchically combining communities and networks. We validate the
correctness of the score by showing that users with higher scores are able to
spread information more effectively in a network. Finally, we use several
comparisons to other ranking systems to show that highly influential and
recognizable users across different domains have high Klout scores.
