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With only token unigrams, the recognition accuracy was 80.5%, while using all features together increased this only slightly to 80.6%. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.

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

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.

However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.

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.

The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below), we find that about 44% of the users are assigned a gender, which is correct in about 87% of the cases.

Another system that predicts the gender for Dutch Twitter users is Tweet Genie ( that one can provide with a Twitter user name, after which the gender and age are estimated, based on the user s last 200 tweets.In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.

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