The Use of Modularity Algorithms as part of the Conceptualization of the Perspectival Form in Large Networks

How can we identify perspectives in large networks through the application of modularity algorithms? In the digital humanities [1][2], there is a fair number of scholarly work exploring computational routines to cluster and analyze enormous amounts of data. Recently, social data became a valuable source to study collective phenomenon, they provide the means to comprehend human collectivity by using graph network analysis. In this paper, we describe our approach on the manner of post-social anthropology [3] and social sciences using technical methods: quantitative analysis and modularity optimization. The computational turn is part of the ongoing process to conceptualize the "perspectival form", as the other would be the semantic analysis of the qualitative data. This technique uses a python script to extract the co-occurrence hashtags network from a Twitter dataset in order to apply in the context of the open-source software Gephi. Our experiments successfully exhibit how social networks can be unfolded when submitting a sample dataset of hashtags to the procedure found in the critical dimension of computational models. Therefore, it discovers the flow of perspectives when the strategy is follow in new workspaces, creating then categories that reveals points of view underneath the controversy. Concluding, this study presents a theoretical and methodological framework based in the post-structuralists, a composition that aims to support studies in different fields of social sciences and humanities.