{"id":547,"date":"2024-12-03T08:55:36","date_gmt":"2024-12-03T08:55:36","guid":{"rendered":"https:\/\/ijaz.me\/?p=547"},"modified":"2024-12-03T09:21:37","modified_gmt":"2024-12-03T09:21:37","slug":"social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro","status":"publish","type":"post","link":"https:\/\/ijaz.me\/index.php\/2024\/12\/03\/social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro\/","title":{"rendered":"A. Kanwal, K. Muhammad Asghar, <b style=\"color: darkblue\">H. Ijazul,<\/b> M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &#8220;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&#8221; Expert Systems with Applications, vol. 255, p. 124650, 2024. <b style=\"color: #b85b00\">(SCI)<\/b>"},"content":{"rendered":"\n<h5 class=\"wp-block-heading\">Abstract<\/h5>\n\n\n\n<p>Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user\u2019s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions and the resultant topologies of content propagation trees. In our study, we have introduced a\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/deep-learning-model\">deep learning model<\/a>\u00a0based on\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/graph-convolutional-network\">Graph Convolutional Neural Networks<\/a>\u00a0(GCNN) and enhanced with multi-head attention mechanisms. This model leverages a wide range of\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/psychology\/psycholinguistics\">psycholinguistic<\/a>\u00a0attributes extracted from users\u2019 posts, including sentiment, emotional content, linguistic features,\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/psychology\/personality-trait\">personality traits<\/a>, readability, and communication style. We further enrich these attributes with\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/bidirectional-encoder-representations-from-transformers\">BERT<\/a>\u00a0embeddings to improve textual representation. Our research framework involves creating two distinct graph networks: the user interaction graph and the semantic propagation graph. These\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/graphical-representation\">graphical representations<\/a>\u00a0are essential for visualizing dynamic interactions and patterns of\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/information-dissemination\">information dissemination<\/a>\u00a0among users. For each user, we generate a set of carefully crafted features that capture their unique characteristics and\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/neuroscience\/behavior-neuroscience\">behaviors<\/a>. These user-specific features are then integrated into the User Interaction Graph, enabling a more nuanced understanding and representation of user interactions within our proposed model. The proposed framework has demonstrated superior performance on benchmark datasets, achieving accuracies and precisions of 91.07% and 91.24% on the FakeNewsNet dataset, and 94.20% and 94.92% on the FibVid dataset, respectively. The results obtained from the experimental evaluation have also revealed the effect of various parameters significant to profile disinformers, signifying a substantial improvement in the field of fake news detection and profiling.<\/p>\n\n\n\n<p>Full Paper: <a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2024.124650\">https:\/\/doi.org\/10.1016\/j.eswa.2024.124650<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user\u2019s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":548,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[22,47,45,46,8,6,33],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A. Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &quot;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&quot; Expert Systems with Applications, vol. 255, p. 124650, 2024. (SCI) - Ijazul Haq<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ijaz.me\/index.php\/2024\/12\/03\/social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A. Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &quot;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&quot; Expert Systems with Applications, vol. 255, p. 124650, 2024. 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This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ijaz.me\/index.php\/2024\/12\/03\/social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro\/\" \/>\n<meta property=\"og:site_name\" content=\"Ijazul Haq\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/ijaz.phd\" \/>\n<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/ijaz.phd\" \/>\n<meta property=\"article:published_time\" content=\"2024-12-03T08:55:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-03T09:21:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ijaz.me\/wp-content\/uploads\/2024\/12\/1-s2.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1173\" \/>\n\t<meta property=\"og:image:height\" content=\"1416\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"ijaz.me\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"ijaz.me\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/ijaz.me\/index.php\/2024\/12\/03\/social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/ijaz.me\/index.php\/2024\/12\/03\/social-medias-dark-secrets-a-propagation-lexical-and-psycholinguistic-oriented-deep-learning-appro\/\"},\"author\":{\"name\":\"ijaz.me\",\"@id\":\"https:\/\/ijaz.me\/#\/schema\/person\/3bb27d2451420d1138b550fcb6de0c7f\"},\"headline\":\"A. Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &#8220;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&#8221; Expert Systems with Applications, vol. 255, p. 124650, 2024. 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Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., \\\"Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,\\\" Expert Systems with Applications, vol. 255, p. 124650, 2024. 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Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &#8220;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&#8221; Expert Systems with Applications, vol. 255, p. 124650, 2024. 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Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &#8220;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&#8221; Expert Systems with Applications, vol. 255, p. 124650, 2024. 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Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., \"Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,\" Expert Systems with Applications, vol. 255, p. 124650, 2024. 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Kanwal, K. Muhammad Asghar, H. Ijazul, M. Alanoud Al, M. S. Syam, I. Nisreen, et al., &#8220;Social media\u2019s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation,&#8221; Expert Systems with Applications, vol. 255, p. 124650, 2024. 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