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Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.

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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.

2004), with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901); (Hotelling 1933)).

We also varied the recognition features provided to the techniques, using both character and token n-grams.

When using all user tweets, they reached an accuracy of 88.0%.

An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy of 92.0%.

In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.

And, obviously, it is unknown to which degree the information that is present is true.

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.

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.

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