Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions. The talk investigates machine learning techniques for modeling social networks and social media. First part will discuss methods for extracting and tracking information as it spreads among users. We will examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. Second part will focus on models for extracting structure from social networks and predicting emergence of new links in social networks. In particular, we will examine methods based on Supervised Random Walks for learning to rank nodes on a graph and consequently recommend new friendships in social networks. We will also consider the problem of detecting dense overlapping clusters in networks and present efficient model based methods for network community detection.