@inproceedings{606fe840e3694bcdb90f58618e32de39,
title = "Age identification of twitter users: Classification methods and sociolinguistic analysis",
abstract = "In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.",
keywords = "Age identification, Computational Sociolinguistics, Sociolinguistics, Text classification, Text mining",
author = "Vasiliki Simaki and Iosif Mporas and Vasileios Megalooikonomou",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-75487-1_30",
language = "English",
isbn = "9783319754864",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature ",
pages = "385--395",
editor = "Alexander Gelbukh",
booktitle = "Computational Linguistics and Intelligent Text Processing - 17th International Conference, CICLing 2016, Revised Selected Papers",
address = "Netherlands",
note = "17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2016 ; Conference date: 03-04-2016 Through 09-04-2016",
}