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Social-media data provides increasing opportunities for automated analysis oflarge sets of textual documents. So far, automated tools have been developed toaccount for either the social networks between the participants of the debates,or to analyze the content of those debates. Less attention has been paid tomapping co-occurring actors (participants) and topics (content) in onlinedebates that form socio-semantic networks. We propose a new, automated approachthat uses a whole matrix approach of co-addressed topics and the actors. Weshow the advantages of the new approach with the analysis of a large set ofEnglish-language Twitter messages at the Rio+20 meeting, in June 2012 (72,077tweets), and a smaller data set of Dutch-language Twitter messages on bird flurelated to poultry farming in 2015-2017 (2,139 tweets). We discuss thetheoretical, methodological and substantive implications of our approach, alsofor the analysis of other social-media data.

Automated Analysis of Topic-Actor Networks on Twitter: New approach to the analysis of socio-semantic networks