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Increasing popularity of Twitter in politics is subject to commercial andacademic interest. To fully exploit the merits of this platform, reaching thetarget audience with desired political leanings is critical. This paper extendsthe research on inferring political orientations of Twitter users to the caseof 2017 Turkish constitutional referendum. After constructing a targeteddataset of tweets, we explore several types of potential features to buildaccurate machine learning based predictive models. In our experiments, athree-class support vector machine (SVM) classifier trained on semanticfeatures achieves the best accuracy score of 89.9%. Moreover, an SVM classifiertrained on full-text features performs better than an SVM classifier trained onhashtags, with respective accuracy scores of 89.05% and 85.9%. Relatively highaccuracy scores obtained by full-text features may point to differences inlanguage use, which deserves further research.

Inferring Political Alignments of Twitter Users: A case study on 2017 Turkish constitutional referendum