Video analysis with the aim of discovering social relations between the people in that video is an important and unexplored topic with significant benefit towards a higher level understanding of videos. This article focuses on the inference of two social groups in each video where members of each group share friendly relations with each other and have an adversarial relation with members of the other social group. Using low-level audiovisual features and motion trajectories we compute a measure of expression of social relation in each scene in video. The occurrence of actors in each scene is computed using face recognition with LBP descriptor. The actor-scene forms a 2-model social network, which we use to compute a 1-mode network of actors. The leaders of each group, which are the actors with greater social impact are estimated using Eigencentrality. We demonstrate our approach on several Hollywood films, which span genres of action, adventure, drama, sci-fi, thriller, historic, and fantasy. This approach is successful at using video content analysis to infer the two social groups and typically the principal protagonist and antagonist in the films as well.