[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123018-en":3,"doc-seo-123018-105":30,"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":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},123018,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Real-Time Fake News Detection on the X (Twitter) - An Online Machine Learning Approach","Fake news amplified by social media has a serious impact on social life, especially in political contexts. Detecting fake news online has gained attention, yet many existing approaches rely on batch traditional machine learning or emergent deep learning learned from training data, which is inefficient for continual real-time detection on unseen streams. This study proposes online machine learning to identify fake news in real time on Twitter, evaluating algorithms such as ALMA and Passive-Aggressive and comparing against prior ML/DL methods. Preliminary results show OML’s adaptability and robustness for dynamic misinformation streams.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| AMCIS 2024 Proceedings | Americas Conference on Information Systems\u003Cbr>(AMCIS) |\n| --- | --- |\n| August 2024\u003Cbr>Real-Time Fake News Detection on the X (Twitter): An Online Machine Learning Approach\u003Cbr>Piyush Vyas\u003Cbr>Texas A&M University-Central Texas, [piyush.vyas@tamuct.edu](piyush.vyas@tamuct.edu)\u003Cbr>Jun Liu\u003Cbr>Dakota State University, [jun.liu@dsu.edu](jun.liu@dsu.edu)\u003Cbr>Shengjie Xu\u003Cbr>University of Arizona, [sjxu@arizona.edu](sjxu@arizona.edu)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/amcis2024](https://aisel.aisnet.org/amcis2024) |  |\n\nRecommended Citation  \nVyas, Piyush; Liu, Jun; and Xu, Shengjie, \"Real-Time Fake News Detection on the X (Twitter): An Online Machine Learning Approach\" (2024) . AMCIS 2024 Proceedings. 15.  \n[https://aisel.aisnet.org/amcis2024/social_comp/social_comput/15](https://aisel.aisnet.org/amcis2024/social_comp/social_comput/15)  \nThis material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in AMCIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nReal-Time Fake News Detection onthe X (Twitter): An Online Machine Learning  \nApproach  \nEmergent Research Forum (ERF) Paper  \nPiyushVyas  \nTexas A&M University-Central Texas [piyush.vyas@tamuct.edu](piyush.vyas@tamuct.edu)  \nJun Liu  \nDakota State University [jun.liu@dsu.edu](jun.liu@dsu.edu)  \nShengjie Xu  \nUniversity of Arizona  \n[sxu14@ieee.org](sxu14@ieee.org)  \nAbstract  \nFake news, along with the speed of mass communication via social media, is having a significant impact on our social life, particularly in the political world. Fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. Existing studies have proposed to employ traditional machine learning (ML) or emergent deep learning (DL) methods to detect fake news. These ML or DL methods based on batch processing and learning from a training dataset, however, are inefficient in conducting continual real-time fake news detection for incoming unseen social media data. In this research, we propose to use online machine learning (OML) to automatically identify fake news in real time. We investigated various online learning algorithms, including Approximate Large Margin Algorithm (ALMA), Passive-Aggressive (PA), etc., and compared their performance with some existing ML or DL methods for real-time fake news detection on Twitter. The preliminary results of our study demonstrate the considerable potential of OML techniques in classifying real-time fake news, thus highlighting the adaptability and robustness of OML in handling dynamic information streams such as fake news.  \nKeywords (Required)  \nOnline machine learning, fake news, misinformation, X, Twitter, and social media.  \nIntroduction  \nThe dissemination of online misinformation carries significant repercussions for individuals, businesses, and society at large. Misinformation, such as fake news, constituting forms of deliberate propaganda, is disseminated intentionally, often through social media platforms. Fake news, a subset of online misinformation, is crafted to deceive and manipulate the public, typically for financial or ideological motives. Its manifestation can range from traditional print to online content. The circulation of fake news is shaped not only by the intentions of content creators but also by the dynamics ofthe propagation process, influenced by individual judgments and global circulation trends. A survey revealed a notable lack of trust in the accuracy and fairness of news among two-thirds of U.S. adults (Watson 2024). Additionally, a Pew Research (2023) study indicated that 55% to 65% of respondents urge governmental and high-tech company int","cbCaie8QwpeS7O0i","https://ap.wps.com/l/cbCaie8QwpeS7O0i","pdf",213011,1,6,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem context\n## Research objective and question\n## Online machine learning approach vs batch learning\n## Research gap and study contribution","[{\"question\":\"Why is real-time fake news detection important on social media?\",\"answer\":\"Fake news spreads rapidly through social platforms and can significantly affect individuals and society, particularly in political settings, creating an urgent need for timely detection.\"},{\"question\":\"How does online machine learning (OML) differ from traditional batch machine learning for this task?\",\"answer\":\"OML updates the model one data point at a time and adapts to new incoming information, while traditional batch ML is trained on the entire dataset at once and may be less responsive to immediate changes.\"},{\"question\":\"Which online learning algorithms are investigated in the study?\",\"answer\":\"The study investigates online learning algorithms including Approximate Large Margin Algorithm (ALMA) and Passive-Aggressive (PA), and compares them with existing ML/DL approaches for Twitter.\"}]","Real-Time Fake News Detection on the X (Twitter) - An Online Machine Learning Approach | PDF",1785814196,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"real-time-fake-news-detection-on-the-x-twitter-an-online-machine-learning-approach","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/real-time-fake-news-detection-on-the-x-twitter-an-online-machine-learning-approach/123018/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",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 real-time fake news detection important on social media?","Question",{"text":76,"@type":77},"Fake news spreads rapidly through social platforms and can significantly affect individuals and society, particularly in political settings, creating an urgent need for timely detection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does online machine learning (OML) differ from traditional batch machine learning for this task?",{"text":81,"@type":77},"OML updates the model one data point at a time and adapts to new incoming information, while traditional batch ML is trained on the entire dataset at once and may be less responsive to immediate changes.",{"name":83,"@type":74,"acceptedAnswer":84},"Which online learning algorithms are investigated in the study?",{"text":85,"@type":77},"The study investigates online learning algorithms including Approximate Large Margin Algorithm (ALMA) and Passive-Aggressive (PA), and compares them with existing ML/DL approaches for Twitter.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]