On Tuning the Randomness in the Random Forest Algorithm for Performance Enhancement Towards Fake Profile Detection in Online Social Platforms

The increased popularity of online social networking platforms has positively and negatively impacted users (humans). Among several others, one significant concern is the presence of fake profiles, which may harm users’ experience and trust. To deal with such an issue, we present our research to focus on detecting counterfeit profiles in online social networking platforms. As an instance of such an online platform, we choose Instagram. In our presented research, we have used a supervised Machine Learning Algorithm known as the Random Forest (RF) due to its flexibility and strength towards ensemble learning technique that is especially useful for classification and regression-related tasks. In our proposed approach, we precisely focus on the possible enhancement of the accuracy and efficiency of the fake profile detection technique with enhanced randomness achieved through seed selection. Experimental results confirm that the proposed model achieved 98.71 percent accuracy, an F1-score of 0.96, and an improved Recall value of 97 percent (from 85 percent), which ensures better detection of fake profiles compared to the Baseline Random Forest model.