In recent years, microblogging services, such as Twitter, emerged as a popular platform for real-time information exchange among millions of users. However, the vast amount of content results in an information overload when searching in microblogs. Given the user's search query, delivering relevant content is a challenging problem. In this thesis, we therefore present three complementary approaches to tackle the challenges of microblog search.

First, we propose a method to determine the quality of content within microblog documents (called \tweets"). To model the quality of tweet content, we devise a set of link-based features, in addition to content-based features. Novel metrics are proposed to reflect quality-based reputation of websites, hashtags and users.

Second, we presen...[ Read more ]

In recent years, microblogging services, such as Twitter, emerged as a popular platform for real-time information exchange among millions of users. However, the vast amount of content results in an information overload when searching in microblogs. Given the user's search query, delivering relevant content is a challenging problem. In this thesis, we therefore present three complementary approaches to tackle the challenges of microblog search.

First, we propose a method to determine the quality of content within microblog documents (called \tweets"). To model the quality of tweet content, we devise a set of link-based features, in addition to content-based features. Novel metrics are proposed to reflect quality-based reputation of websites, hashtags and users.

Second, we present two frameworks to model topics discussed in microblogs. In our Multi-faceted Topic Modeling framework, we tackle both the short length of tweets and the rich semantics discussed by microblog users. We first perform semantic enrichment to inject additional semantics into the short tweets. We then model latent topics that comprise the social terms in Twitter, auxiliary terms from external URLs and named entities. In our Geographic Twitter Topic Modeling framework, we focus on spatial aspects of microblog topics. We propose a content-based method for extracting locations from tweets and model the rich interplay between microblog topics and locations.

Third, we present a framework for Collaborative Personalized Twitter Search. Traditional techniques for personalized Web search are insufficient in the microblog domain, because of the diversity of topics, sparseness of user data and the highly social nature. Our framework introduces a topic-aware user model structure to manage topical diversity. We then develop a collaborative user model, which exploits the user's social connections to obtain a comprehensive account of her preferences. A detailed evaluation has demonstrated a superior ranking performance of our framework compared with state-of-the-art baselines.