A Machine Learning-Based Approach for Automated Filtering and Blocking of Objectionable Web Content: Design and Implementation

This work aimed to develop a machine learning objectionable web content filtering and blocking system. This was important because of the high level of proliferation of objectionable web content which had posed a significant challenge to maintain a safe and appropriate online environment. The objectives of the study include a developed machine learning system that can send a real-time short message system(sms) to parents or guardians or other designated individuals responsible for the children’s safety informing them when their children had opened objectionable web content, a developed machine learning system that kept log of objectionable web content if network is unavailable and send notification if network is restored, a developed machine learning system endowed with enough reasoning capability to intelligently filter web objectionable content and ill-suited web pages, a developed machine learning system that was capable to have kept detailed log of objectionable web content even if the search was carried out in private (incognito mode in goggle chrome). The programming language of choice used in this work was python since its codes can be created quicker and performed faster than many other programming languages. The methodology adopted was the object-oriented Analysis and Design methodology (OOADM). The study utilized Anaconda Jupiter Notebook as its development environment, python as its programming language and then SQlite as the Database Management System (DBMS). The machine learning web content filtering and blocking system serves as a powerful tool to protect our children from harmful online content, promoting a safer and more secured digital environment.