Anonymous Threat Creators Detection in Social media using Support Vector Machine technique with Improved Accuracy Compared with Random Forest Technique

The main objective of the study is to control Anonymous Threat creators detection in social media using Innovative Support Vector Machine technique with improved accuracy compared with Random Forest Technique. Prediction of Anonymous Threat creators detection using Innovative Support Vector Machine algorithm (N = 10) and Random Forest algorithm (N = 10). Innovative Support Vector Machine is a supervised machine learning detection algorithm, Random Forests are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter.Anonymous Threat creators detection dataset is used for prediction of Threat creators in social media. The accuracy of the Anonymous Threat creators detection in social media using Innovative Support Vector Machine algorithm is 90.18% and Random Forest Algorithm is 85.22% with G power 80%. Innovative Support Vector Machine uses kernel tricks to solve non-linear problems whereas Random Forests derive hyper-rectangles in input space to solve the problem. There exists a statistically significant difference between the two groups (p=0.014; p¡0.05) with confidence interval 95%. Support Vector Machine algorithm seems to be more accurate than the Random Forest Algorithm in predicting the Anonymous Threat creators detection in social media.