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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%.Their highest score when using just text features was 75.5%, testing on all the tweets by each author (with a train set of 3.3 million tweets and a test set of about 418,000 tweets). (2012) used SVMlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets.

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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.

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.

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

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