Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science

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With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result"/> <meta name="keywords" content="Machine Learning, Cross Validation, Training and Prediction, Misinformation"/>   <meta name="dc.title" content="Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science"> <meta name="citation_authors" content="Kevin Qu"> <meta name="citation_authors" content="Yu Sun"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT) Vol. 11, No.23"> <meta name="dc.date" content="2021/12/24"> <meta name="dc.identifier" content="10.5121/csit.2021.112327"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="A number of social issues have been grown due to the increasing amount of “fake news”. With the inevitable exposure to this misinformation, it has become a real challenge for the public to process the correct truth and knowledge with accuracy. In this paper, we have applied machine learning to investigate the correlations between the information and the way people treat it. With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result. "/> <meta name="dc.subject" content="Machine Learning"> <meta name="dc.subject" content="Cross Validation"> <meta name="dc.subject" content="Training and Prediction"> <meta name="dc.subject" content="Misinformation">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2021/12/24"> <meta name="prism.volume" content="11"> <meta name="prism.number" content="23"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="355">   <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Kevin Qu and Yu Sun"> <meta name="citation_title" content="Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science"> <meta name="citation_online_date" content="2021/12/24"> <meta name="citation_issue" content="11"> <meta name="citation_firstpage" content="355"> <meta name="citation_authors" content="Kevin Qu"> <meta name="citation_authors" content="Yu Sun"> <meta name="citation_doi" content="10.5121/csit.2021.112327"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v11n23/csit112327.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol11/csit112327.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/abstract/v11n23/csit112327.html"> <meta property="og:title" content="Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science"> <meta property="og:description" content="A number of social issues have been grown due to the increasing amount of “fake news”. With the inevitable exposure to this misinformation, it has become a real challenge for the public to process the correct truth and knowledge with accuracy. In this paper, we have applied machine learning to investigate the correlations between the information and the way people treat it. With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result. 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With the inevitable exposure to this misinformation, it has become a real challenge for the public to process the correct truth and knowledge with accuracy. In this paper, we have applied machine learning to investigate the correlations between the information and the way people treat it. With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Machine Learning, Cross Validation, Training and Prediction, Misinformation.</p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol11/csit112327.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/csit/V11N23.html">Volume 11, Number 23</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>

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Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science