Most of the popular multimedia sharing web-sites such as YouTube, Flickr etc not only allow users to author and upload content but also facilitate 'social' networking amongst users. These social interactions can be in the form of - user-to-user interactions i.e. adding existing users to friend or contact list or user-to-content interactions: commenting on a video or picture, marking a picture/video as 'favorite', subscribing to a user created 'channel' etc. Analyzing these social interactions jointly with the content metadata (such as the description of the video, keywords associated with the image/video etc) can reveal interesting insights about user activity on these social media platforms. In this paper, we propose an unsupervised method that jointly models 'social' interaction and content metadata in YouTube to discover user-communities and the nature of topics beings discussed in these communities. We report the effectiveness of the proposed method on real-world dataset.