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Some of the most pioneering work in our field is occurring where emerging computational approaches are meeting time series analytic techniques. Combining these methods is helping scholars improve our understanding of phenomena as varied as news and issue attention cycles, physiological responses to communication exposure, changes in mass opinion, and the dynamics between social media and legacy news media. In this article, we summarize the current state of computational communication science techniques to generate sequential data for use in time series analysis and suggest directions for further development. In particular, we consider the long-standing place of temporal dynamics for our field’s main theories; overview recent work combining computational science with time series analysis; present narrative accounts of two major research programs in this area; and review techniques of time series analysis, including major concerns for communication researchers working in the area.
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