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If no cue is found in a user s profile, no gender is assigned.The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below), we find that about 44% of the users are assigned a gender, which is correct in about 87% of the cases.

2009) managed to increase the gender recognition quality to 89.2%, using sentence length, 35 non-dictionary words, and 52 slang words.With lexical N-grams, they reached an accuracy of 67.7%, which the combination with the sociolinguistic features increased to 72.33%. (2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (SVM), Naive Bayes and Balanced Winnow2.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.These statistics are derived from the users profile information by way of some heuristics.For gender, the system checks the profile for about 150 common male and 150 common female first names, as well as for gender related words, such as father, mother, wife and husband.

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