Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithm

Abstract

Social media platforms, such as Twitter, Instagram, and Facebook, have facilitated mass communication and connection. Due to the development as well as the advancement of social platforms, the spreading of fake news has increased. Many studies have been performed for detecting fake news with machine learning algorithms; but these existing methods had several difficulties, such as rapid propagation, access method and insignificant selection of features, and low accuracy of the text classification. Therefore, to overcome these issues, this paper proposed a hybrid Bidirectional Encoder Representations from Transformers — Support Vector Machine (BERT-SVM) model with a recommendation system that used to predict whether the information is fake or real. The proposed model consists of three phases: preprocessing, feature selection and classification. The dataset is gathered from Twitter social media related to COVID-19 real-time data. Preprocessing stage comprises Splitting, Stop word removal, Lemmatization and Spell correction. Term Frequency Inverse Document Frequency (TF-IDF) converter is utilized to extract the features and convert text to binary vectors. A hybrid BERT-SVM classification model is used to predict the data. Finally, the predicted data is compared with the preprocessed data. The proposed model is implemented in MATLAB software with several performance metrics carried out, and these parameters attained better performance: accuracy is 98 %, the error is 2 %, precision is 99 %, specificity is 99 %, and sensitivity is 98 %. Therefore the better effectiveness of the proposed model than existing approaches is shown. The proposed social networking analysis model provides effective fake news prediction that can be used to identify the Twitter comments, either real or fake.