[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126521-en":3,"doc-seo-126521-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126521,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved","Despite years of attention and reported breakthroughs, misinformation containment continues to grow without clear signs of relief. Misinformation evolves rapidly in hidden characteristics and spreads aggressively in multi-modal forms, sometimes causing harms beyond those of typical malware. This chapter surveys NLP and machine learning research to curb misinformation spread, evaluates why existing work is not yet practical for integration into social media, and outlines future research directions. It also discusses state-of-the-art feature engineering, approaches, and algorithms.","San Jose State University  \nSJSU ScholarWorks  \nFaculty Research, Scholarly, and Creative Activity  \n1-1-2023  \nMisinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved  \nVishnu S. Pendyala  \nSan Jose State University, [vishnu.pendyala@sjsu.edu](vishnu.pendyala@sjsu.edu)  \nFollow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca)  \nRecommended Citation  \nVishnu S. Pendyala. \"Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved\" Deep Learning Research Applications for Natural Language Processing (2023): 41-56.  \n[https://doi.org/10.4018/978-1-6684-6001-6.ch003](https://doi.org/10.4018/978-1-6684-6001-6.ch003)  \nThis Contribution to a Book is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \n41  \nChapter 3  \nMisinformation Containment Using NLP and Machine Learning:  \nWhy the Problem Is Still Unsolved  \nVishnu S. Pendyala  \n [https://orcid.org/0000-0001-6494-7832](https://orcid.org/0000-0001-6494-7832)  \nSan Jose State University, USA  \nABSTRACT  \nDespite the increased attention and substantial research into it claiming outstanding successes, the problem of misinformation containment has only been growing in the recent years with not many signs of respite. Misinformation is rapidly changing its latent characteristics and spreading vigorously in a multi-modal fashion, sometimes in a more damaging manner than viruses and other malicious programson the internet. This chapter examines the existing research in natural language processing and machine learning to stop the spread of misinformation, analyzes why the research has not been practical enough to be incorporated into social media platforms, and provides future research directions. The state-of-theart feature engineering, approaches, and algorithms used for the problem are expounded in the process.  \nINTRODUCTION  \nSocial media has been subject to plenty of controversies owing to its use for spreading misinformation, sometimes to the extent of manipulating a country’s presidential elections (Pendyala et al., 2018) . The objective of this chapter is to explain some of the recent machine learning and natural language processing approaches for misinformation containment and provide reasons why, despite the large quantity of research in the area, the problem is still unsolved. Modeling domains using math has time and again proven to yield solutions to some of the toughest problems in the past. Machine learning, for the most part, has evolved from applied math. There has been an upsurge in the literature on the topic of trust in social media using machine learning models in recent times. This chapter starts with a survey of some  \nDOI: [10.4018/978-1-6684-6001-6.ch003](10.4018/978-1-6684-6001-6.ch003)  \nCopyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.  \nMisinformation Containment Using NLP and Machine Learning  \nof the machine learning models, methods, and techniques that have been used to address the problem of the trustworthiness of the information on the Internet, which helps in misinformation containment.  \nThe techniques are discussed under various sub-heads such as language models, few-shot learning, bot detection, graph theoretic approaches to misinformation containment, and using Generative Adversarial Network models for detecting fake multimedia content as well as textual content. As Table 1 shows, the corpus of articles on this topic is tremendous. A comprehensive survey of the existing literature is beyond the scope of this work. The survey is mainly intended to convey the underlying techniques and the res","cbCaiodtqVuOXmgw","https://ap.wps.com/l/cbCaiodtqVuOXmgw","pdf",576900,2,1,17,"English","en",105,"# Introduction\n## Survey of NLP and machine learning approaches\n## Reported success vs. unresolved challenges\n# Background\n## Fake news as a continuing major problem","[{\"question\":\"Why is misinformation containment still considered unsolved despite extensive research?\",\"answer\":\"Reported successes have not translated into practical, effective solutions for real platform integration, and the problem keeps growing as misinformation evolves and spreads in multi-modal ways.\"},{\"question\":\"Which research approaches are reviewed for stopping misinformation spread?\",\"answer\":\"The chapter examines NLP and machine learning methods, including language models, few-shot learning, bot detection, graph-based approaches, and GAN-based techniques for detecting fake multimedia and text.\"},{\"question\":\"What future directions does the chapter suggest?\",\"answer\":\"It highlights remaining challenges in solving misinformation containment and points to areas for further work, emphasizing progress needed for usability on social media platforms.\"}]","Misinformation Containment Using NLP and Machine Learning: Why the Problem Is Still Unsolved | PDF",1785933137,43,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"misinformation-containment-using-nlp-and-machine-learning-why-the-problem-is-still-unsolved","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/misinformation-containment-using-nlp-and-machine-learning-why-the-problem-is-still-unsolved/126521/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is misinformation containment still considered unsolved despite extensive research?","Question",{"text":76,"@type":77},"Reported successes have not translated into practical, effective solutions for real platform integration, and the problem keeps growing as misinformation evolves and spreads in multi-modal ways.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which research approaches are reviewed for stopping misinformation spread?",{"text":81,"@type":77},"The chapter examines NLP and machine learning methods, including language models, few-shot learning, bot detection, graph-based approaches, and GAN-based techniques for detecting fake multimedia and text.",{"name":83,"@type":74,"acceptedAnswer":84},"What future directions does the chapter suggest?",{"text":85,"@type":77},"It highlights remaining challenges in solving misinformation containment and points to areas for further work, emphasizing progress needed for usability on social media platforms.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]