This paper describes our framework to annotate events using personal and social network contexts. The problem is important as the correct context is critical to effective annotation. Social network context is useful as real-world activities of members of the social network are often correlated, within a specific context. There are two main contributions of this paper: (a) development of an event context framework and definition of quantitative measures for contextual correlations based on concept similarity (b) recommendation algorithms based on spreading activations that exploit personal context as well as social network context. We have very good experimental results. Our user study with real world personal images indicates that context (both personal and social) facilitates effective image annotation.