Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
This work provides a tool for improving interest modeling in social networks, but it is incremental as it builds on existing ontology and labeling approaches.
The paper introduces Klout Topics, a lightweight ontology for modeling social media users' interests and expertise, and demonstrates its coverage and application to over 780 million users on Klout.com.
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.