Klout Topics for Modeling Interests and Expertise of Users Across Social Networks

TL;DR

This paper introduces Klout Topics, a human-readable, extensible ontology for modeling social media users' interests and expertise across multiple domains, supporting interest analysis for over 780 million users.

Contribution

It presents a new lightweight, comprehensive ontology designed for interest modeling and interest labeling in social media, with open access for external use.

Findings

Coverage of interests across social media platforms

Application to modeling interests of over 780 million users

Comparison with existing interest modeling alternatives

Abstract

This paper presents Klout Topics, a lightweight ontology to describe social media users' topics of interest and expertise. Klout Topics is designed to: be human-readable and consumer-friendly; cover multiple domains of knowledge in depth; and promote data extensibility via knowledge base entities. We discuss why this ontology is well-suited for text labeling and interest modeling applications, and how it compares to available alternatives. We show its coverage against common social media interest sets, and examples of how it is used to model the interests of over 780M social media users on Klout.com. Finally, we open the ontology for external use.