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We achieved the best results, 95.5% correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams.

Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL data set: a daily updated collection that probably contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013).

In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.

They used lexical features, and present a very good breakdown of various word types.

When using all user tweets, they reached an accuracy of 88.0%.

A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work), reaching about 80% correct attributions using function words and parts of speech.

Later, in 2004, the group collected a Blog Authorship Corpus (BAC; (Schler et al.

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