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Classifying misinformation of User Credibility in Social Media Using Supervised Learning

Computers, Materials & Continua 2023年第5期75卷 2921-2938页

作者： Muhammad Asfand-e-Yar Qadeer Hashir Syed Hassan Tanvir Wajeeha Khalil Department of Computer Science Center of Excellence in Artificial Intelligence(CoE-AI)Bahria UniversityIslamabad44000Pakistan Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25000Pakistan

The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for any user to spread misinformation through the ***,misinformation is a problem for professionals,organizers,and ***,it is essential to observe the credibility and validity of the News articles being shared on social *** core challenge is to distinguish the difference between accurate and false *** studies focus on News article content,such as News titles and descriptions,which has limited their ***,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user *** the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and *** calculate user credibility and combine it with article content with the user’s *** combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or ***,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required *** dataset contains 23000+articles with millions of user *** highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest *** discoveries confirmed that the proposed classifier would effectively classify misinformat

关键词： misinformation user credibility fake news machine learning

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Investigating the influential factors of consumer's credibility judgment on health misinformation

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Journal of Library Science in China 2019年 167-168页

作者： SONG Shijie ZHAO Yuxiang SONG Xiaokang ZHU Qinghua 不详

Health information seeking has a long *** information empowers consumers to make informed health decisions and plays significant roles in many health activities such as consumers’self-diagnose,chronic disease management,and patient-physician *** effectiveness of such empowerment is based on one common assumption,namely the information *** accurate information could foster consumer’s informed health decisions,while distorted information could lead to severe health crises.

关键词： Investigating consumer’s misinformation

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Examining data visualization pitfalls in scientific publications

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 268-282页

作者： Vinh T Nguyen Kwanghee Jung Vibhuti Gupta Department of Information Technology TNU–University of Information and Communication TechnologyThai NguyenVietnam Department of Educational Psychology Leadershipand CounselingTexas Tech UniversityLubbockTX 79409United States Department of Computer Science and Data Science Meharry Medical CollegeNashvilleTN 37208USA

Data visualization blends art and science to convey stories from data via graphical *** different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full *** the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input *** a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and *** might be even worse if incorrect visual representations were intentionally produced to deceive the *** address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical *** reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial ***,a text mining technique was applied to extract practical insights from common visualization ***’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial *** findings showed that the pie chart is the most misused graphical representation,and size is the most critical *** was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.

关键词： Data visualization Graphical representations misinformation Visual encodings Association rule mining Word cloud Cochran’s Q test McNemar’s test

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A systematic survey on deep learning and machine learning approaches of fake news detection in the pre-and post-COVID-19 pandemic

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 617-646页

作者： Rajshree Varma Yugandhara Verma Priya Vijayvargiya Prathamesh P.Churi Computer Engineering Narsee Monjee Institute of Management and Higher StudiesMumbaiIndia

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 *** the coronavirus disease 2019(COVID-19)pandemic,individuals are inflicted with these false and potentially harmful claims and stories,which may harm the vaccination *** 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 *** 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’***/methodology/approach-The detailed literature review on fake news detection is divided into three major *** authors searched papers no later than 2017 on fake news detection approaches on deep learning andmachine *** paperswere initially searched through theGoogle scholar platform,and they have been scrutinized for *** authors kept“Scopus”and“Web of Science”as quality indexing *** 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 ***-The paper is dissected into two approaches,namely machine learning and deep learning,to present a better understanding and a clear ***,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 *** paper also delves into fake new detec

关键词： Fake news detection Machine learning Deep learning Artificial intelligence Natural language processing COVID-19 Social media misinformation

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Domestic Cyberterrorism & Strategic Communications: Literature Review

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Journal of Information Security 2023年第4期14卷 472-489页

作者： Robb Shawe Ian R. McAndrew Department of Cybersecurity Leadership Capitol Technology University Laurel MD USA

Cyberterrorism poses a significant threat to the national security of the United States of America (USA), with critical infrastructure, such as commercial facilities, dams, emergency services, food and agriculture, healthcare and public health, and transportation systems virtually at risk. Consequently, this is due primarily to the country’s heavy dependence on computer networks. With both domestic and international terrorists increasingly targeting any vulnerabilities in computer systems and networks, information sharing among security agencies has become critical. Cyberterrorism can be regarded as the purest form of information warfare. This literature review examines cyberterrorism and strategic communications, focusing on domestic cyberterrorism. Notable themes include the meaning of cyberterrorism, how cyberterrorism differs from cybercrime, and the threat posed by cyberterrorism to the USA. Prevention and deterrence of cyberterrorism through information sharing and legislation are also key themes. Finally, gaps in knowledge are identified, and questions warranting additional research are outlined.

关键词： Cyberterrorism Cybersecurity Information Sharing Act Mal-Information misinformation Disinformation Fake News Propaganda Strategic Communications

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