A message classifier based on multinomial Naive Bayes for online social contexts

Children and teenagers today are increasingly connected to the internet. The use by minors of social networks applications, and games that are connected to the internet offer the possibility of communication, can make them exposed to various threats. One of the most troubling threats is sexual abuse. Thus the objective of this project is to create a model for classifying messages, as normal or dangerous, according to the risk they present to the minor. In addition to integrating the developed model with a project that analyzes the behavior of minors in a social network (Facebook), and calculates the risk of the minor be a victim of sexual abuse. Finally, we use the model in the classification of messages obtained from a server of the game Minecraft, quite popular among children.