Automatic estimation of web bloggers’ age using regression models

Vasiliki Simaki, Christina Aravantinou, Iosif Mporas, Vasileios Megalooikonomou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In this article, we address the problem of automatic age estimation of web users based on their posts. Most studies on age identification treat the issue as a classification problem. Instead of following an age category classification approach, we investigate the appropriateness of several regression algorithms on the task of age estimation. We evaluate a number of well-known and widely used machine learning algorithms for numerical estimation, in order to examine their appropriateness on this task. We used a set of 42 text features. The experimental results showed that the Bagging algorithm with RepTree base learner offered the best performance, achieving estimation of web users’ age with mean absolute error equal to 5.44, while the root mean squared error is approximately 7.14.

Original languageEnglish
Title of host publicationSpeech and Computer - 17th International Conference, SPECOM 2015, Proceedings
EditorsAndrey Ronzhin, Rodmonga Potapova, Nikos Fakotakis
PublisherSpringer Nature
Number of pages8
ISBN (Print)9783319231310
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event17th International Conference on Speech and Computer, SPECOM 2015 - Athens, Greece
Duration: 20 Sept 201524 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Speech and Computer, SPECOM 2015


  • Author’s age estimation
  • Regression algorithms
  • Text processing


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