Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Bridging the Gap: A Genre Analysis of Weblogs
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Age and geographic inferences of the LiveJournal social network
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Predicting age and gender in online social networks
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Inferring personal traits from music listening history
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
User demographics and language in an implicit social network
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Content-based similarity measures of weblog authors
Proceedings of the 5th Annual ACM Web Science Conference
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
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We investigate whether wording, stylistic choices, and online behavior can be used to predict the age category of blog authors. Our hypothesis is that significant changes in writing style distinguish pre-social media bloggers from post-social media bloggers. Through experimentation with a range of years, we found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age prediction. We also show that internet writing characteristics are important features for age prediction, but that lexical content is also needed to produce significantly more accurate results. Our best results allow for 81.57% accuracy.