The timeframe of my PhD studies has coincided with the emergence and worldwide spread of social media. These include blogging and microblogging platforms (e.g. Blogger.com and Twitter.com), social networking sites (e.g. Facebook.com and MySpace.com), as well as platforms that allow for the sharing and annotation of content (e.g. Flickr.com and YouTube.com).

The popularity and versatility of social platforms has lead to the accumulation

of overwhelming volumes of diverse information. As demonstrated by numerous research works, mining such data can further our understanding of these platforms and help us improve the online social experience of their users.

My own work has focused on addressing some of the major algorithmic challenges that emerge in the process of mining social data.

In particular, I have always found search-based problems to be the most intriguing. On a high-level, the primary objective of my research has been to bridge the gap between users and information in a social context. From a research point of view, I have always been interested in mining two particular types of corpora: graph structures and textual data, both of which are abundant in social media. In this document, I discuss the relevant problems that I have tackled during my studies. The discussion of each problem is accompanied by an appropriate formulation, algorithmic techniques for its solution, and extensive experimental evaluations.