Purpose-The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news chann...Purpose-The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels,freelance reporters and websites.Amid the coronavirus disease 2019(COVID-19)pandemic,individuals are inflicted with these false and potentially harmful claims and stories,which may harm the vaccination process.Psychological studies reveal that the human ability to detect deception is only slightly better than chance;therefore,there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate.This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre-and post-pandemic,which has never been done before to the best of the authors’knowledge.Design/methodology/approach-The detailed literature review on fake news detection is divided into three major parts.The authors searched papers no later than 2017 on fake news detection approaches on deep learning andmachine learning.The paperswere initially searched through theGoogle scholar platform,and they have been scrutinized for quality.The authors kept“Scopus”and“Web of Science”as quality indexing parameters.All research gaps and available databases,data pre-processing,feature extraction techniques and evaluationmethods for current fake news detection technologies have been explored,illustrating them using tables,charts and trees.Findings-The paper is dissected into two approaches,namely machine learning and deep learning,to present a better understanding and a clear objective.Next,the authors present a viewpoint on which approach is better and future research trends,issues and challenges for researchers,given the relevance and urgency of a detailed and thorough analysis of existing models.This paper also delves into fake new detection during COVID-19,and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/value-The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful,although currently reported accuracy has not yet reached consistent levels in the real world.展开更多
基金The author would like to thank the anonymous reviewers and respected editors for taking valuable time to go through the manuscript.
文摘Purpose-The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels,freelance reporters and websites.Amid the coronavirus disease 2019(COVID-19)pandemic,individuals are inflicted with these false and potentially harmful claims and stories,which may harm the vaccination process.Psychological studies reveal that the human ability to detect deception is only slightly better than chance;therefore,there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate.This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre-and post-pandemic,which has never been done before to the best of the authors’knowledge.Design/methodology/approach-The detailed literature review on fake news detection is divided into three major parts.The authors searched papers no later than 2017 on fake news detection approaches on deep learning andmachine learning.The paperswere initially searched through theGoogle scholar platform,and they have been scrutinized for quality.The authors kept“Scopus”and“Web of Science”as quality indexing parameters.All research gaps and available databases,data pre-processing,feature extraction techniques and evaluationmethods for current fake news detection technologies have been explored,illustrating them using tables,charts and trees.Findings-The paper is dissected into two approaches,namely machine learning and deep learning,to present a better understanding and a clear objective.Next,the authors present a viewpoint on which approach is better and future research trends,issues and challenges for researchers,given the relevance and urgency of a detailed and thorough analysis of existing models.This paper also delves into fake new detection during COVID-19,and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/value-The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful,although currently reported accuracy has not yet reached consistent levels in the real world.
