In recent years, enterprise group chat collaboration tools such as Slack, IBM's Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools-productivity increase and improved group communication-come significant challenges. Specifically, users find it hard to focus their attention on content that is relevant to them due to the load of conversational content. This load can be handled by personalized content presentation and summarization mitigated by user profiling. We present an unsupervised approach for implicitly modeling group chat users through a combination of a probabilistic topic model and social analysis. We evaluate our approach by testing it on a task of conversation participation prediction, serving as a proxy for anticipating user interests, and show that by utilizing our approach, a system successfully predicts users participation in conversations. We further analyze the contribution of the various user model components and show them to be significant.