Abstract
In today's digital era, the majority of social networking site users prefer communication through oral or video means. Unfortunately, this trend has led to the spread of inaccurate information for two primary reasons. Firstly, individuals knowingly or unknowingly make false claims and influence others to do the same, creating an illusion of accuracy and leading to poor decision-making. Secondly, information related to minors under the age of 13 is being disseminated through websites and social networking platforms. This poses a challenge for young individuals to make informed choices as many people tend to believe and act upon such information. While social networking sites offer various benefits, they also come with inherent risks. To address these issues, we propose a preventive measure using a pre-translated framework system called Deep Support Vector Machine (DSVM). This innovative approach combines a Support Vector Machine (SVM) with a Deep Neural Network (DNN) to effectively reduce these hazards. We emphasize the importance of promoting positive and accurate information about kids, while strictly disallowing the spread of any false or misleading content about minors. In our paper, we thoroughly contrast and evaluate the effectiveness of our suggested DSVM method with the well-established preventive techniques of SVM and LSTM. By doing so, we aim to devise a more robust and reliable system to ensure a safer and more responsible use of social networking sites, particularly when it comes to sharing information about minors.