Semantic Enrichment of Twitter Posts for User Pro le Construction on the Social Web

Abstract

As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.