Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

作     者：Asma Qaiser Saman Hina Abdul Karim Kazi Saad Ahmed Raheela Asif 

作者机构：Department of Computer Science and Information TechnologyNED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer ScienceIqra UniversityKarachi76400Pakistan Department of Software EngineeringNED University of Engineering and TechnologyKarachi75270Pakistan 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：73-90页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

主　　题：Deep learning fake news detection machine learning transformer model classification 

摘      要：In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.