The evolution of the Web brought new interesting problems to computer scientists that we loosely classify in the fields of social and semantic computing. Social computing is related to two major paradigms: computations carried out by a large amount of people in a collective intelligence fashion (i.e. wikis), and performing computations on social networks (i.e. social network analysis). On the other hand, semantic computing focuses on assigning meaning to data and enabling computers to process and integrate it (i.e. natural language processing, data mining, etc.). This dissertation delves into the intersection of social and semantic computing. We present new hybrid techniques showing how the two domains benefit each other or are used together to produce better content. These synergistic techniques introduce an extra level of complexity that can be alleviated by the use of interfaces. User interfaces not only increase the transparency and explanation in systems where social and semantic computing are used together, but also serve as input modalities that additionally boost the quality of the content produced by the hybrid techniques. We exemplify our contributions with three case studies of novel user interfaces that utilize social and semantic computing.