Conference Proceeding Article
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.
Age topic model, Gibbs-EM, Lexical variation
Computer Sciences | Databases and Information Systems | Social Media
Data Management and Analytics
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence: 27-31 July 2014, Québec
City or Country
Palo Alto, CA
LIAO, Lizi; JIANG, Jing; DING, Ying; HUANG, Heyan; and LIM, Ee Peng.
Lifetime Lexical Variation in Social Media. (2014). Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence: 27-31 July 2014, Québec. 1643-1649. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2414
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