Identifying the Authors' National Variety of English in Social Media Texts
In this paper, we present a study for the identification of authors{'} national variety of English in texts from social media. In data from Facebook and Twitter, information about the author{'}s social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and data-based features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32{\%}. The experimental results are evaluated, and the efficacy of the ranked features discussed.
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