Discriminating Between Closely Related Languages on Twitter" (Informatica, 2015)
Editorial: "In this paper we tackle the problem of discriminating Twitter users by the language they tweet in, takinginto account very similar South-Slavic languages – Bosnian, Croatian, Montenegrin and Serbian. Weapply the supervised machine learning approach by annotating a subset of 500 users from an existingTwitter collection by the language the users primarily tweet in. We show that by using a simple bag-ofwordsmodel, univariate feature selection, 320 strongest features and a standard classifier, we reach userclassification accuracy of 98%. Annotating the whole 63,160 users strong Twitter collection with the bestperforming classifier and visualizing it on a map via tweet geo-information, we produce a Twitter languagemap which clearly depicts the robustness of the classifier."