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When addressing public concerns such as the existence of politically likeminded communication spaces in social media, analyses of complex political discourses are met with increasing methodological challenges to process communication data properly. To address the extent of political like-mindedness in online communication, we argue that it is necessary to focus not only on ideological homogeneity in online environments, but also on the extent to which specific political questions are discussed in a uniform manner. This study proposes an innovative combination of computational methods, including natural language processing and social network analysis, that serves as a model for future research examining the evolution of opinion climates in online networks. Data were gathered on YouTube, enabling the assessment of users’ expressed opinions on three political issues (i.e., adoption rights for same-sex couples, headscarf rights, and climate change). Challenging widely held assumptions on discursive homogeneity online, the results provide evidence for a moderate level of connections between dissimilar YouTube comments but few connections between agreeing comments. The findings are discussed in light of current computational communication research and the vigorous debate on the prevalence of like-mindedness in online networks.

Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network Analysis