Title: Text-Based Age and Gender Prediction for Online Safety Monitoring

Issue Number: Vol. 5, No. 1
Year of Publication: Feb - 2016
Page Numbers: 46-60
Authors: Janneke van de Loo , Guy De Pauw, Walter Daelemans
Journal Name: International Journal of Cyber-Security and Digital Forensics (IJCSDF)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002012


This paper explores the capabilities of text-based age and gender prediction geared towards the application of detecting harmful content and conduct on social media. More specifically, we focus on the use case of detecting sexual predators who try to “groom” children online and possibly provide false age and gender in-formation in their user profiles. We perform age and gender classification experiments on a dataset of nearly 380,000 Dutch chat posts from a social network. We evaluate and compare binary age classifiers trained to separate younger and older authors according to differ-ent age boundaries and find that macro-averaged F-scores increase when the age boundary is raised. Fur-thermore, we show that use-case applicable perfor-mance levels can be achieved for the classification of minors versus adults, thereby providing a useful com-ponent in a cybersecurity monitoring tool for social network moderators.