Lifetime Lexical Variation in Social Media


  • Lizi Liao Beijing Institute of Technology
  • Jing Jiang Singapore Management University
  • Ying Ding Singapore Management University
  • Heyan Huang Beijing Institute of Technology
  • Ee-Peng Lim Singapore Management University



Age topic model, Gibbs-EM, Lexical variation


As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific topics such as “school” for teenagers and “health” for older people. Our model can also be used for age prediction and performs better than a number of baseline methods.




How to Cite

Liao, L., Jiang, J., Ding, Y., Huang, H., & Lim, E.-P. (2014). Lifetime Lexical Variation in Social Media. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).