Performance Evaluation of Multiple Classifiers for Predicting Fake News  

作　　者：Arzina Tasnim Md. Saiduzzaman Mohammad Arafat Rahman Jesmin Akhter Abu Sayed Md. Mostafizur Rahaman Arzina Tasnim;Md. Saiduzzaman;Mohammad Arafat Rahman;Jesmin Akhter;Abu Sayed Md. Mostafizur Rahaman(Department of Information Systems Security, Bangladesh University of Professionals, Dhaka, Bangladesh;Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh;Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh)

机构地区：[1]Department of Information Systems Security, Bangladesh University of Professionals, Dhaka, Bangladesh [2]Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh [3]Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

出　　处：《Journal of Computer and Communications》2022年第9期1-21,共21页电脑和通信（英文）

摘　　要：The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

关 键 词：Fake News Machine Learning TF-IDF CLASSIFIER Estimator F1 Score RECALL Precision Voting Classifiers Stacking Classifier Soft Voting Hard Voting 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]