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We achieved the best results, 95.5% correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams.

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

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.

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%.

172 For Tweets in Dutch, we first look at the official user interface for the Twi NL data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches.

Then follow the results (Section 5), and Section 6 concludes the paper. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. the identification of author traits like gender, age and geographical background.

In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.

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

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.

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