Discovering users' topics of interest on twitter: a first look

Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called "Tweets." In this paper we present early results on discovering Twitter users' topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a "topic profile," which characterizes users' topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--clustering; I.2.7 [Artificial Intelligence]: Natural Language Processing--text analysis General Terms Algorithms, Experimentation

• Type: Journal
• Year: 2010
• Where: AND
• Authors: Matthew Michelson, Sofus A. Macskassy
• Keywords: AND 2010, Entities, General Terms Algorithms, Machine Learning, Tweets