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They used lexical features, and present a very good breakdown of various word types.

When using all user tweets, they reached an accuracy of 88.0%.

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.

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.

Last modified 05-Mar-2020 17:01