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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.

The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.

For gender, the system checks the profile for about 150 common male and 150 common female first names, as well as for gender related words, such as father, mother, wife and husband.

For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. The creators themselves used it for various classification tasks, including gender recognition (Koppel et al. The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions.

One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study (Goswami et al.

For our experiment, we selected 600 authors for whom we were able to determine with a high degree of certainty a) that they were human individuals and b) what gender they were.

We then experimented with several author profiling techniques, namely Support Vector Regression (as provided by LIBSVM; (Chang and Lin 2011)), Linguistic Profiling (LP; (van Halteren 2004)), and Ti MBL (Daelemans et al.

Computational Linguistics in the Netherlands Journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting of the full Tweet production (as far as present in the Twi NL data set) of 600 users (known to be human individuals) over 2011 and We experimented with several authorship profiling techniques and various recognition features, using Tweet text only, in order to determine how well they could distinguish between male and female authors of Tweets.

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.

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.

For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.

In the following sections, we first present some previous work on gender recognition (Section 2). Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. (2012) show that authorship recognition is also possible (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies).

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