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Predicting Author Age from Weibo Microblog Posts
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Predicting Author Age from Weibo Microblog Posts
We report an author pro ling study based on Chinese social media texts gleaned from Sina Weibo (新浪ž�ō�) in which we attempt to predict the authors age group based on various linguistic text features mainly relating to non-standard orthography: classical Chinese characters, hashtags, emoticons and kaomoji, homogeneous punctuation and Latin character sequences, and poetic format. We also tracked the use of selected popular Chinese expressions, parts-of-speech and word types. We extracted 100 posts from 100 users in each of four age groups (under-18, 19-29, 30-39, over-40 years) and by clustering users posts fifty at a time we trained a maximum entropy classifier to predict author age group to an accuracy of 65.5%. We show which features are associated with younger and older age groups, and make our normalisation resources available to other researchers.
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