ABSTRACT
In this chapter, we describe our unconventional mixing of methods, which brings together techniques from the humanities, social sciences, and computer science that rarely come into contact. Our methods add a humanistic lens of linguistic and rhetorical textual analysis to a computational approach to social media and its influence on the contemporary political landscape in the era of post-truth. To illuminate the novel contributions of our method, we trace a short multidisciplinary history of the study of networks – manifested as sociograms, social network analysis (SNA), network science, and actor-network theory. To sharpen our methodological contrast to existing studies, we also describe sentiment analysis and its rise over the past decade as the predominant technical method for studying affect and emotion on social media networks. The chapter concludes with an explanatory and statistical analysis of the networks we generated from the time of the COVID-19 pandemic as our test case. Our hybrid approach combining natural language processing and SNA gives us a dual field of vision to unpack the layers of meaning defining the behavior of emotional and rational language as they shape social media debates.
