Developing a Protective – Preventive and Machine Learning Based Model on Child Abuse

EasyChair Preprint no. 4963

Abstract

Online grooming is an ever-increasing problem in societies and the time spent online is recently started to rise drastically. People can become anonymous whilst posting, sharing his/her own opinion, and being a part of online chatting. Option to be anonymous also brings together the chance for hiding personal identity when making an attempt on illegal activities. Online grooming is one of the significant areas of aforementioned actions and sexual predators can easily use online chatting platforms to quickly build a friendly relationship with children or teenagers to gain their trust and make them share their obscene media files. These sexual predators mostly try to convince their victims to meet and it may lead to having sexual intercourse with a minor. In order to draw attention to the huge challenge that most societies face, this study mainly aims to identify predators in the early stage of online communication. The objective is to do an investigation to detect child grooming through online chat records by using Machine Learning techniques. In the first part of the study, it has been achieved to make a multi-label classification on a Wikipedia dataset with more than 97 percent accuracy, where a given text gets classified based on the toxicity types. The outcome of this work is also used in the second stage and herein PAN12 dataset has been used to train and test our model. We have ended up with more than 92 percent accuracy, where suspicious conversation messages from the chat records get identified and sexual predators can be recognized.

Keyphrases: Child Abuse Detection, machine learning, Online Sexual Predator Identification, text classification

@Booklet{EasyChair:4963,
  author = {Fatih Mert and Muhammed Ali Aydin and Abdül Halim Zaim},
  title = {Developing a Protective – Preventive and Machine Learning Based Model on Child Abuse},
  howpublished = {EasyChair Preprint no. 4963},

  year = {EasyChair, 2021}}