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In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.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.
We also varied the recognition features provided to the techniques, using both character and token n-grams.
The age component of the system is described in (Nguyen et al. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus.
The paper does not describe the gender component, but the first author has informed us that the accuracy of the gender recognition on the basis of 200 tweets is about 87% (Nguyen, personal communication). (2014) did a crowdsourcing experiment, in which they asked human participants to guess the gender and age on the basis of 20 to 40 tweets. on this, we will still take the biological gender as the gold standard in this paper, as our eventual goal is creating metadata for the Twi NL collection. Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments (Section 3.1).
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.
If no cue is found in a user s profile, no gender is assigned.
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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.