The article introduces a multiple-perspective co-citation analysis method to characterize and interpret the structure and dynamics of co-citation clusters in the field of Information Science. The method integrates network visualization, spectral clustering, automatic cluster labeling, and text summarization to facilitate analytic tasks and enhance interpretability. The study applies this method to a three-part analysis of the field, including a comparative author co-citation analysis (ACA), a progressive ACA of a time series of co-citation networks, and a progressive document co-citation analysis (DCA). The results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks. The article also discusses the strengths and limitations of the new approach and suggests directions for future research.The article introduces a multiple-perspective co-citation analysis method to characterize and interpret the structure and dynamics of co-citation clusters in the field of Information Science. The method integrates network visualization, spectral clustering, automatic cluster labeling, and text summarization to facilitate analytic tasks and enhance interpretability. The study applies this method to a three-part analysis of the field, including a comparative author co-citation analysis (ACA), a progressive ACA of a time series of co-citation networks, and a progressive document co-citation analysis (DCA). The results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks. The article also discusses the strengths and limitations of the new approach and suggests directions for future research.