Towards Building a Versatile Tool for Social Media Spam Detection
Jalal Abdel Halim, Weiqing Sun · 2023
With the rapid increase of social network spam, it's essential to empower users with the tool to detect the harmful spam effectively. However, existing tools cannot fully meet the requirements. In the paper, we propose and develop a live detection tool that can detect spam text and images from social networks. This tool will be trained on user collected data in the form of both images and text using different machine learning classifiers, the users are then able to save the model and load it whenever they want to use a social network, where this tool will show the users a notification alerting them whether the post they are looking at is spam before they even get the chance to read the text or look at the image, thus protecting them from the potentially malicious information or links. Performance and functional evaluation results have demonstrated the effectiveness of our tool.