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2009) managed to increase the gender recognition quality to 89.2%, using sentence length, 35 non-dictionary words, and 52 slang words.

Their features were hash tags, token unigrams and psychometric measurements provided by the Linguistic Inquiry of Word Count software (LIWC; (Pennebaker et al. Although LIWC appears a very interesting addition, it hardly adds anything to the classification.

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

And, obviously, it is unknown to which degree the information that is present is true.

The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.

In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques.

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