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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.
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.
Later, in 2004, the group collected a Blog Authorship Corpus (BAC; (Schler et al.
2006)), containing about 700,000 posts to (in total about 140 million words) by almost 20,000 bloggers. Slightly more information seems to be coming from content (75.1% accuracy) than from style (72.0% accuracy). We see the women focusing on personal matters, leading to important content words like love and boyfriend, and important style words like I and other personal pronouns.
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.
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).