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Computational communication delves into the analysis of digital data, social media interactions, and algorithms that shape communication processes, yet few studies focus on the framework and internal structure of the methodological framework related to adaptive topics. This study employs text mining techniques to analyze 9795 publications from international scientific citation databases, and outlines a classification framework to describe the methods used in empirical research. The framework highlights traditional quantitative methods and new computational methods. The former conduct statistical analysis on medium-sized and structured samples, while the latter provides microscopic outlooks with extensive data analysis. Experimental results show the thematic distribution, evolution phases, and subject boundaries of the method categories. This study expands the scope of social computing methodology and provides a wealth of empirical insights.
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