[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123306-en":3,"doc-seo-123306-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123306,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Techniques for Identifying Textual Propaganda on Social Media - Development of a Detecting Digital Manipulation System","The dissemination of propaganda on social media creates major challenges for information integrity and democratic participation. Advanced tools and diverse strategies are used to shape public opinion at scale, making these platforms key channels for manipulation. This research develops a digital detection system to determine whether published content qualifies as propaganda. It tests the hypothesis that propaganda can be distinguished from non-propaganda texts through specific linguistic features, using linguistic analysis and machine learning to reach about 70% accuracy, supporting more transparent and reliable online information.","ISSN ONLINE: 2447-0228  \nITEGAM-JETIA  \nManaus, v.11 n.53, p. 43-49. May/June., 2025. DOI: [https://doi.org/10.5935/jetia. v11i53.1443](https://doi.org/10.5935/jetia. v11i53.1443)  \n| RESEARCH ARTICLE |  |  | OPEN ACCESS |\n| --- | --- | --- | --- |\n| MACHINE LEARNING TECHNIQUES FOR IDENTIFYING TEXTUAL PROPAGANDA ON SOCIAL MEDIA: DEVELOPMENT OF A DETECTING\u003Cbr>DIGITAL MANIPULATION SYSTEM\u003Cbr>Belkacem mostefai1, Tarek Boutefara2 , Abid Chahinez3 and Aberkane Marwa4\u003Cbr>1 Faculty of Exact Sciences and Computer Science, Djelfa University, 17000 DZ, Djelfa, Algeria.\u003Cbr>2,3,4 Faculty of Exact Sciences and Computer Sciences, University of Jijel,18000 DZ Jijel, Algeria.\u003Cbr>1[http://orcid.org/ 0000-0002-2118-8407](http://orcid.org/ 0000-0002-2118-8407) , 2http://orcid.org/0000-0002-7222-9387 , 3 http://orcid.org/0009-0001-5469-5878 ,\u003Cbr>4[http://orcid.org/0009-0001-7771-3098](http://orcid.org/0009-0001-7771-3098) \u003Cbr>[Email: b.mostefai@univ-djelfa.dz](Email: b.mostefai@univ-djelfa.dz), [t_boutefara@univ-jijel.dz](t_boutefara@univ-jijel.dz), [abidshahinez@gmail.com](abidshahinez@gmail.com), [aberkanmerwa@gmail.com](aberkanmerwa@gmail.com) |  |  |  |\n| ARTICLE INFO |  | ABSTRACT\u003Cbr>The dissemination of propaganda on social media presents a significant challenge in today’s digital age. Utilizing advanced tools and diverse methods, propaganda aims to influence public opinion on a massive scale. Social media platforms serve as prime channels for such messages, leveraging sophisticated strategies to shape public perceptions and attitudes. This research aims to develop an advanced system capable of evaluating whether the content disseminated on these platforms qualifies as propaganda. The hypothesis suggests that it is possible to distinguish propaganda from non-propaganda texts on social media by analyzing specific linguistic features. Employing advanced linguistic analysis and machine learning methods, this detection system achieves approximately 70% accuracy, indicating its promising potential for effectively identifying propaganda. This approach could significantly enhance the transparency and reliability of online information, encouraging amore informed and critical use of social media. |  |\n| Article History\u003Cbr>Received: November 30, 2024\u003Cbr>Revised: January 20, 2025\u003Cbr>Accepted: May 15, 2025\u003Cbr>Published: May 31, 2025 |  |  |  |\n| Keywords:\u003Cbr>Propaganda Detection, Social media,\u003Cbr>Machine Learning Techniques, Linguistic Analysis,\u003Cbr>Digital manipulation, |  |  |  |\n|  | Copyright ©2025 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM) . This work is licensed under the Creative Commons Attribution International License (CC BY 4.0) . |  |  |\n\nI. INTRODUCTION  \nPropaganda is a powerful tool for shaping collective perceptions and influencing individual behavior [1] . While it has historically been used in political, cultural, and commercial contexts, its integration with social media has amplified its impact, making it a potent mechanism for widespread influence [2], [3] . With vast user networks, social media platforms enable the rapid and viral spread of propaganda, posing significant threats to democracy, public health, and social cohesion. Initially rooted in political and religious campaigns, propaganda has evolved into sophisticated digital strategies [4]-[6] . The spread of propaganda on social media alters the global perception of events, particularly in conflict situations [7], [8] . The use of these platforms to disseminate unverified and sensationalist content misleads and reinforces unfounded prejudices. This distortion of reality severely hampers international conflicts, exacerbates societal divisions, and undermines trust in institutions, highlighting the urgent need to  \npromote fact-checking and critical thinking in the contemporary media landscape. [9], [10] .  \nPropaganda often employ persuasive techniques such as emotional appeals, misinformation, and targeted messaging to manipulate publi","cbCaip53cBfZfe1L","https://ap.wps.com/l/cbCaip53cBfZfe1L","pdf",1049399,1,7,"English","en",105,"# Introduction\n## Background and impact of propaganda on social media\n## Key persuasive and dissemination techniques\n## Machine learning and NLP approaches\n# Methodology\n## Dataset and annotation strategy\n## Text preprocessing and normalization\n## Feature extraction with TF-IDF\n## Model development for propaganda classification","[{\"question\":\"What problem does the research address on social media?\",\"answer\":\"The research addresses the challenge of identifying propaganda content disseminated on social media and its potential to mislead users and distort public perception.\"},{\"question\":\"How does the proposed system distinguish propaganda from non-propaganda texts?\",\"answer\":\"It analyzes specific linguistic features and uses linguistic analysis combined with machine learning to separate propaganda from non-propaganda language.\"},{\"question\":\"What performance level does the detection approach report?\",\"answer\":\"The method achieves approximately 70% accuracy, indicating promising potential for identifying propaganda effectively.\"}]","Machine Learning Techniques for Identifying Textual Propaganda on Social Media - 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