We develop and study two social network-based algorithms for automatically computing authors' reputations from a collection of textual documents. First, given a set of documents, both algorithms examine keyword reference behaviors of the authors to construct a social network. This social network represents the relationship among the authors in terms of information reference behavior. With the resulting network, the first algorithm computes each author's reputation value considering only direct referential activities while the second considers indirect activities as well. We discuss the reputation values computed by the two algorithms and compare them with the reputation ratings given by a human domain expert. We also analyze the social network through a community detection algorithm. We observed several interesting phenomena including the network being scale-free and having negative assortativity.