Dynamic Light Weight Recommendation System for Social Networking Analysis Using a Hybrid LSTM-SVM Classifier Algorithm

Abstract

Social media has become one of the significant platforms for information sharing. At that same time, the influence of fake news is a growing cause for all people using social networking platforms. The entire world faces a difficult situation with the COVID-19 pandemic. Simultaneously, the leaking of info in social media concerning COVID-19 also increases exponentially. These information cause serious worries, which affects people psychologically. Several social media analysis methods are developed over years. However, those had several difficulties due to short text social media comments, which causes significant data sparsity. To overcome such difficulties, this paper proposed a recommendation system for social networking to predict whether the information is fake or real using a hybrid LSTM-SVM classifier. Initially, the proposed model gathered real-time COVID-19 related commands from Twitter social media to form a dataset. The collected data is preprocessed by splitting, stop word removal, lemmatization, and spell correction. After preprocessing, the features from the data are extracted and converted to binary with the assist of a count vectorizer. The obtained features are further classified with a hybrid LSTM-SVM model. The predicted data is compared with the preprocessed data, consisting of real information. If the predicted data is equal to the preprocessed data, it will be real news or else fake news. The proposed model is implemented to attain better performance. Some of the performance metrics such as accuracy, sensitivity, specificity, and error are 90, 88, 97, and 0.1% respectively for the proposed model. The overall expected outcome of the recommendation system using hybrid LSTM-SVM is better than the existing techniques such as CNN-SVM, GRNN, LSTM, CNN, and SVM. The Hybrid LSTM-SVM model attained the best accuracy for predicting fake or real news.