Fake or Genuine? Contextualised Text Representation for Fake Review Detection

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>Fake or Genuine? Contextualised Text Representation for Fake Review Detection</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Fake or Genuine? Contextualised Text Representation for Fake Review Detection"> <meta name="description" content="Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models"/> <meta name="keywords" content="Fake review, detection, Transformer, Ensemble, Deep learning"/>   <meta name="dc.title" content="Fake or Genuine? Contextualised Text Representation for Fake Review Detection "> <meta name="citation_authors" content="Rami Mohawesh"> <meta name="citation_authors" content="Shuxiang Xu"> <meta name="citation_authors" content="Matthew Springer"> <meta name="citation_authors" content="Muna Al-Hawawreh"> <meta name="citation_authors" content="Sumba Maqsood"> <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="2021/12/21"> <meta name="dc.identifier" content="https://doi.org/10.5121/csit.2021.112311"> <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="Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models."> <meta name="dc.subject" content="Fake review"> <meta name="dc.subject" content="detection"> <meta name="dc.subject" content="Transformer"> <meta name="dc.subject" content="Deep learning">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="2021/12/21"> <meta name="prism.volume" content="11"> <meta name="prism.number" content="23"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="137">   <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="Rami Mohawesh,Shuxiang Xu,Matthew Springer,Muna Al-Hawawreh and Sumba Maqsood"> <meta name="citation_title" content="Fake or Genuine? Contextualised Text Representation for Fake Review Detection "> <meta name="citation_online_date" content="2021/12/21"> <meta name="citation_issue" content="23"> <meta name="citation_firstpage" content="137"> <meta name="citation_authors" content="Rami Mohawesh"> <meta name="citation_authors" content="Shuxiang Xu"> <meta name="citation_authors" content="Matthew Springer"> <meta name="citation_authors" content="Muna Al-Hawawreh"> <meta name="citation_authors" content="Sumba Maqsood"> <meta name="citation_doi" content="https://doi.org/10.5121/csit.2021.112311"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v11n23/csit112311.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol11/csit112311.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/papers/vol11/csit112311.pdf"> <meta property="og:title" content="Fake or Genuine? Contextualised Text Representation for Fake Review Detection"> <meta property="og:description" content="Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. 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Contextualised Text Representation for Fake Review Detection</a></h4> <h3>  Authors</h3> <p class="#left right" style="text-align:">Rami Mohawesh<sup>1</sup>, Shuxiang Xu<sup>1</sup>, Matthew Springer<sup>1</sup>, Muna Al-Hawawreh<sup>2</sup> and Sumbal Maqsood<sup>1</sup>, <sup>1</sup>University of Tasmania, Australia, <sup>2</sup>University of New South Wales, Australian Defence Force Academy (ADFA), Australia</p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Fake review, detection, Transformer, Ensemble, Deep learning.</p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol11/csit112311.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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Fake or Genuine? Contextualised Text Representation for Fake Review Detection