Klout Score: Measuring Influence Across Multiple Social Networks

TL;DR

The paper introduces the Klout Score, a comprehensive influence metric across multiple social networks, leveraging extensive interaction data and a hierarchical model to quantify user influence effectively.

Contribution

It presents a novel hierarchical framework for measuring influence across multiple social networks using a large feature set and scalable data processing techniques.

Findings

Higher Klout scores correlate with greater information spreading ability.

The score effectively identifies influential users across different domains.

Validation shows the score aligns with real-world influence indicators.

Abstract

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.