Online media is an important part of modern information society, offering a podium for public discourse and hosting the opinions of hundreds of millions of individuals. Online media are often credited for providing a technological means to break information barriers and promote diversity and democracy. In practice, however, the opposite effect is often observed: users tend to favor content that agrees with their existing world-view, get less exposure to conflicting viewpoints, and eventually create information silos and increased polarization. Arguably, without any kind of mediation, current social-media platforms gravitate towards a state in which net-citizens are constantly reinforcing their existing opinions. In this project we will develop theoretical foundations and a concrete set of algorithmic techniques to address deficiencies in today's online media. We will develop methods to discover structure and patterns of segregation, conflict, and closeness in social-media systems. We will address the issues of reducing bias and polarization, breaking information silos, and creating awareness of users to explore alternative viewpoints. We will also study the effect of different design features to the willingness of the users to explore viewpoints that conflict their opinion. The project is structured along three interwined research thrusts: knowledge discovery, exploration, and content recommendation. To accomplish its aims the project will formulate novel problem representations that provide a deeper understanding of the undesirable phenomena observed in online media and allow for effective remedial actions. Strong emphasis will be given on designing algorithms that are scalable to large data, are able to deal with uncertainty, and offer theoretical guarantees. The end result will be a set of new methods and tools that will contribute to increasing exposure to diverse ideas and improving online deliberation.