Predicting Author Age from Weibo Microblog Posts
Wanru Zhang, Andrew Caines, Dimitrios Alikaniotis, Paula J. Buttery · 2016
We report an author profiling study based on Chinese social media texts gleaned from Sina Weibo (新浪微博) in which we attempt to predict the author's 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.