Threat Detector for Social Media Using Text Analysis
Sadi, Saidul Haq, Hossain Pk, Md Rubel, Akram M. Zeki · The International Islamic University Malaysia Repository (The International Islamic University Malaysia) · 2021
The scam is one of the most important security threats among social media users. It is required to detect scams not only to protect social user's data that is stored online but also to secure the social network. Besides, machine learning techniques are becoming more popular in the text analysis sector. To fraud detection, the most used supervised machine learning techniques are Naïve Bayes (NB) and Support vector machine (SVM). In this project, a machine learning model is developed for detecting threats from Twitter tweets. Accordingly, the Naïve Bayes classifier and flask micro web framework were used to build the model by using the python programming language. The model provided 91% accuracy in detecting tweet scam threats. This finding will benefit the social network users to be aware of threats as well as social media network providers to enhance their security system.