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For our experiment, we selected 600 authors for whom we were able to determine with a high degree of certainty a) that they were human individuals and b) what gender they were.
However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.
With only token unigrams, the recognition accuracy was 80.5%, while using all features together increased this only slightly to 80.6%. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.
They used lexical features, and present a very good breakdown of various word types.
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.
These statistics are derived from the users profile information by way of some heuristics.
When using all user tweets, they reached an accuracy of 88.0%.