Automated Analysis of Topic-Actor Networks on Twitter: New approach to the analysis of socio-semantic networks
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Social-media data provides increasing opportunities for automated analysis of large sets of textual documents. So far, automated tools have been developed to account for either the social networks between the participants of the debates, or to analyze the content of those debates. Less attention has been paid to mapping co-occurring actors (participants) and topics (content) in online debates that form socio-semantic networks. We propose a new, automated approach that uses a whole matrix approach of co-addressed topics and the actors. We show the advantages of the new approach with the analysis of a large set of English-language Twitter messages at the Rio+20 meeting, in June 2012 (72,077 tweets), and a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological and substantive implications of our approach, also for the analysis of other social-media data.
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