Bridging semantic and social network analyses: the case of the hashtag #precisamosfalarsobreaborto (we need to talk about abortion) on Twitter

This paper presents an innovative method design that combines semantic with social network analysis in order to measure opinion leadership in social networking sites in a more accurate way. We used this method to assess the efficacy of the TPM magazine in disseminating its pro-decriminalization of abortion frames (contained in the cover story of its 148th issue) that were associated with the hashtag #precisamosfalarsobreaborto (a trending topic in November 2014). The data were collected from Twitter through the data-mining application NodeXL (N?=?1010). A content analysis of a random sample was carried out (N?=?376; margin of error?=?4%; confidence interval?=?95%; Krippendorff’s alpha?=?0.661). Using the software Gephi, we plotted the data on a socio-semantic graph, which indicates that (a) the border of the social network does not represent a semantic gap with the center and (b) despite the network being extremely like-minded, one of its hubs appears to be what we conceptualize as a hotspot of contestation. We discuss how future research may replicate and refine our methodology to handle population datasets and big data as well.