Online communities are becoming important in all aspects of our lives, including businesses, health care, personal relations and so on. Very successful examples of online communities include social networks, such as Facebook, Twitter, etc., and other systems based on user reviews and their interactions, such as eBay, Amazon, Yelp, Epinions, etc. Online communities generate large amounts of data that contain useful information mixed with noise. The noise can be random (for example some irrelevant reviews) or on purposely injected (various attacks, such as fake information). In our real life’s social interactions we rely heavily on trust, similarly, in online communities, we can use trust to filter out all kinds of noises and extract the useful information.  Therefore, we have developed develop a modular and general Trust Management Framework, to capture the specificity of given communities and apply common rules of trust, both based on social science results on human trust. We have applied our trust framework successfully on Twitter and Epinions. Furthermore, we are using it in multi-party decision making, as well as in distributed computing environments.