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Less attention has been paid to mapping co\u2010occurrences of actors (participants) and topics (content) in online debates that can be considered as socio\u2010semantic networks. We propose a new, automated approach that uses the whole matrix of co\u2010addressed topics and actors for understanding and visualizing online debates. We show the advantages of the new approach with the analysis of two data sets: first, a large set of English\u2010language Twitter messages at the Rio\u2009+\u200920 meeting, in June 2012 (72,077 tweets), and second, a smaller data set of Dutch\u2010language Twitter messages on bird flu related to poultry farming in 2015\u20132017 (2,139 tweets). 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