Summary of the paper

Title Predicting Author Age from Weibo Microblog Posts
Authors Wanru Zhang, Andrew Caines, Dimitrios Alikaniotis and Paula Buttery
Abstract We report an author pro ling study based on Chinese social media texts gleaned from Sina Weibo (新浪žō) 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.
Topics Social Media Processing, Person Identification, Profiling
Full paper Predicting Author Age from Weibo Microblog Posts
Bibtex @InProceedings{ZHANG16.958,
  author = {Wanru Zhang and Andrew Caines and Dimitrios Alikaniotis and Paula Buttery},
  title = {Predicting Author Age from Weibo Microblog Posts},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portorož, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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