Lists in social networks have become popular tools to orga-nize content. This paper proposes a novel framework for rec-ommending lists to users by combining several features thatjointly capture their personal interests. Our contribution is oftwo-fold. First, we develop a ListRec model that leveragesthe dynamically varying tweet content, the network of twitterers and the popularity of lists to collectively model the users’preference towards social lists. Second, we use the topicalinterests of users, and the list network structure to developa novel network-based model called the LIST-PAGERANK.We use this model to recommend auxiliary lists that are morepopular than the lists that are currently subscribed by theusers. We evaluate our ListRec model using the Twitterdataset consisting of 2988 direct list subscriptions. Using au-tomatic evaluation technique, we compare the performanceof the ListRec model with different baseline methods andother competing approaches and show that our model deliversbetter precision in terms of the prediction of the subscribedlists of the twitterers. Furthermore, we also demonstrate the importance of combining different weighting schemes andtheir effect on capturing users’ interest towards Twitter lists.To evaluate the LIST-PAGERANK model, we employ a user-study based evaluation to show that the model is effective inrecommending auxiliary lists that are more authoritative thanthe lists subscribed by the users.