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As we approached the task from a machine learning viewpoint, we needed to select text features to be provided as input to the machine learning systems, as well as machine learning systems which are to use this input for classification. Another interesting group of authors is formed by the misclassified ones.

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Then we describe our experimental data and the evaluation method Section 3after which we proceed to describe the various author profiling strategies that we investigated Section 4. When running the underlying systems 7. Then, we used a set of feature types based on token n-grams, with which we already had previous experience Van Bael and van Halteren However, even style appears to mirror content.

This type of character n-gram has the clear advantage of not needing any preprocessing in the form of tokenization. Top Function 4: The conclusion is not so much, however, Dating 60 plussers humans are also not perfect at guessing age on the basis of language use, but rather that there is Dating 60 plussers distinction between the biological and the social identity of authors, and language use is more likely to represent the social one cf.

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In the following sections, we first present some previous work on gender recognition Section 2. And, obviously, it is unknown to which degree the information that is present is true.

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These statistics are derived from the users profile information by way of some heuristics. We first describe the features we used Section 4.

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In scores, too, we see far more variation. For all feature types, we used only those features which were observed with at least 5 authors in our whole collection for skip bigrams 10 authors.

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The most obvious male is authorwith a resounding Looking at his texts, we indeed see a prototypical young male Twitter user: If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated. We will only look at the final scores for each combination, and forgo the extra detail of any underlying separate male and female model scores which we have for SVR and LP; see above.

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We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors. Unigrams Single tokens, similar to the top function words, but then using all tokens instead of a subset.

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We used the most frequent, as measured on our tweet collection, of which the example tweet contains the words ik, dat, heeft, op, een, voor, and het.