Grasshopper-Based Detection of Fake Social Media Profiles

The proliferation of fake profiles on social media platforms presents a growing challenge for digital ecosystems, where the detection of such profiles is critical to maintaining the integrity of online environments. This paper introduces a hybrid approach that integrates the Grasshopper Optimization Algorithm with various Machine Learning classifiers, including Support Vector Machine, Naive Bayes, and Random Forest. The nature-inspired metaheurisitic used is employed to optimize key hyperparameters of these classifiers, thereby enhancing their performance in detecting fake profiles. The proposed method is evaluated on a well defined balanced dataset, demonstrating significant improvements in classification performance, particularly in terms of accuracy, precision, recall, and F1-score. The results suggest that the proposed hybrid approach can effectively address the challenges associated with balanced and imbalanced datasets in fake profile detection. Furthermore, the study discusses potential directions for improving scalability and applying the approach to larger and more dynamic datasets in the future.