Two Sides of the Same Coin: How to Integrate Social Network Analysis and Topic Detection to Investigate Shared...

This paper advances the integration of Social Network Analysis (SNA) and topic detection into the study of Social Representations (SRs). We suggest that a combination of the two analyses helps to detect communities characterised by shared contents and/or social interactions, the two facets that make representations 'social'. Building on Moliner's (2023) proposal we present a step-by-step approach to combine the identification of shared meanings based on lexicometric analysis and identification of social interaction based on social network analysis techniques. To illustrate our proposal, we use a dataset of 396 Brazilian tweets about the Covid-19 pandemic that was collected to investigate the SR of science during the pandemic. The Reinert method was run on the corpus using the Iramuteq R interface and a bipartite network analysis was performed using Gephi software. We thus operationalised 615 users and six topics as nodes, while shared topics and interactions (883 mentions) as arcs. This allowed us to examine both the content of social representations and interactions among different individuals and communities. In our case, the results highlight shared content as the main determinant for community formation; however, some users appear to have linked different communities together: they are associated to a community not because of the topic they share, but because of their interactions with other users. We contend this methodology proves to be a fruitful theoretical-methodological link between SNA and SR theory, as it detects both facets of the relationship between SRs and groups: the shared contents and the communicative interactions between individuals.