Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminating gender on Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We propose a new method for estimating user attributes (gender, age, occupation, and interests) of a Twitter user from the user's contents (profile document and tweets) and social neighbors, i.e. those whom the user has mentioned. Our labeling method is able to collect a large amount of training data automatically by using Twitter users associated with a blog account. Furthermore, we experiment estimation methods using social neighbors with three adjustable levels of its information and show that our method, which uses the target user's profile document and tweets and the neighbors' profile documents (not including tweets), achieves the best accuracy.