With the rise of Computational Social Science (CSS), researchers increasingly rely on big data and computational methods to analyze mediated environmental communication. This chapter summarizes characteristics, opportunities, and limitations of such methodological advances to analyze communication about the environment. It illustrates that access to big data is characterized by reliance on application programming interfaces, databases, crawling, and scraping, which often require programming skills. Researchers use automated content analysis or network analysis as examples of computational methods to measure a range of variables: the salience of communication, actors, communities, positioning, or framing. Opportunities related to the rise of CSS include access to more and different data, disaggregation of data, and analyses across time and space. However, important limitations concern inadequate measurements of complex constructs, for example, frames, and reliance on convenience samples, for instance via Twitter. The chapter closes with key recommendations, including the avoidance of “black box” commercial software tools, an increased need for validation, and a turn towards open science.
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