Topic modeling from network spread
Abstract
Topic modeling refers to the task of inferring, only from data, the abstract ``topics" that occur in a collection of content. In this paper we look at latent topic modeling in a setting where unlike traditional topic modeling (a) there are no/few features (like words in documents) that are directly indicative of content topics (e.g. un-annotated videos and images, URLs etc.), but (b) users share and view content over a social network. We provide a new algorithm for inferring both the topics in which every user is interested, and thus also the topics in each content piece. We study its theoretical performance and demonstrate its empirical effectiveness over standard topic modeling algorithms.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 16, 2014
- Source ID
- 10.1145/2637364.2592018
Entities
People
- Avik Ray
- Sanjay Shakkottai
- Sujay Sanghavi
Organizations
- Army Research Office
- Defense Threat Reduction Agency
- Division of Computer and Network Systems
- Division of Information and Intelligent Systems
- University of Texas at Austin