Text-Based Age and Gender Prediction for Online Safety Monitoring

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 information 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 different age boundaries and find that macro-averaged Fscores increase when the age boundary is raised. Furthermore, we show that use-case applicable performance levels can be achieved for the classification of minors versus adults, thereby providing a useful component in a cybersecurity monitoring tool for social network moderators.