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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.

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).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.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.

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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).

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