[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124353-en":3,"doc-seo-124353-105":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},124353,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Enhancing Fake News Detection via Stance Analysis - Leveraging Advanced NLP Techniques and Machine Learning Models","Fake news detection remains an emerging research area, now drawing growing societal attention. This study investigates stance detection to identify misinformation by modeling the relationship between article headlines and their corresponding body text. Using the FNC-1 and FARN datasets, it applies advanced NLP and machine learning models such as logistic regression, XGBoost, and DistilBERT. Preprocessing includes lemmatization, NER, sentiment analysis, and semantic similarity. Transformer-based methods, especially DistilBERT, achieve stronger performance for nuanced stance classification, supporting accurate, scalable real-world deployment.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 19 No. 11 (2025) |   \n[https://doi.org/10.3991/ijim.v19i11.55007](https://doi.org/10.3991/ijim.v19i11.55007)  \nPAPER  \nEnhancing Fake News Detection via Stance Analysis: Leveraging Advanced NLP Techniques and Machine Learning Models  \nMërgim H. Hoti(􀀍), Festina Qorrolli, Fisnik Spahija  \nUniversity of Prishtina, Prishtinë, Republic of Kosovo [mergim.hoti@uni-pr.edu](mergim.hoti@uni-pr.edu)  \nABSTRACT  \nFake news detection is still a field of research that is in its infancy, and this is clearly evident as it has only recently gained significant attention from society. The use of machine learning algorithms and natural language processing (NLP) techniques offers valuable problem-solving opportunities to address these complex challenges. This study explores stance detection as a method to identify misinformation by examining the connection between article headlinesand their corresponding body text. Utilizing the FNC-1 and FARN datasets, we apply advanced NLP methods and machine learning (ML) models, including logistic regression, XGBoost, and DistilBERT. Key preprocessing techniques such as lemmatization, named entity recognition (NER), sentiment analysis, and semantic similarity are employed to capture both linguistic and contextual features. The experimental results show that transformer-based models such as DistilBERT achieve superior performance compared to traditional approaches, particularly in accurately classifying nuanced stances. These findings highlight the crucial role of context-aware models in improving the accuracy of misinformation detection and demonstrate their potential for scalable, real-world applications.  \nKEYWORDS  \nfake news detection, stance detection, natural language processing (NLP), machine learning (ML), transformer models, misinformation analysis  \n1 INTRODUCTION  \nAs widely acknowledged, news serves as a valuable means of acquiring information on specific issues, where individuals share their experiences on both general and specialized topics. However, not all news provides accurate information, particularly in light of technological advancementsand the potential for misuse. Therefore, careful consideration of the source from which one obtains news should be prioritized equally with the importance of the news content itself. Failure to verify or identify credible sources  \nHoti, M. H., Qorrolli, F., Spahija, F. (2025) . Enhancing Fake News Detection via Stance Analysis: Leveraging Advanced NLP Techniques and Machine Learning Models. International Journal of Interactive Mobile Technologies (iJIM), 19(11), pp. 39–50. [https://doi.org/10.3991/ijim.v19i11.55007](https://doi.org/10.3991/ijim.v19i11.55007)[ ](https://doi.org/10.3991/ijim.v19i11.55007)[Article submitted 2025-01-18. Revision uploaded 2025-03-08. Final acceptance 2025-03-12.](Article submitted 2025-01-18. Revision uploaded 2025-03-08. Final acceptance 2025-03-12.)  \n© 2025 by the authors of this article. Published under CC-BY.  \niJIM | Vol. 19 No. 11 (2025) International Journal of Interactive Mobile Technologies (iJIM) 39  \nHoti et al.  \ncan result in the spread of misinformation, impacting a significant number of readers. The spread of misinformation especially impacts society, politics, and the economy by threatening public health and disrupting democratic processes. It quickly circulates through social platforms and forums, eroding trust in credible sources and intensifying societal polarization. The use of various techniques in daily life, particularly in the field of AI, is a challenging and complex process due to the underlying frameworks it employs. Similarly, the objective of this paper is to detect fake news through stance detection by applying various algorithms from natural language processing (NLP) and machine learning (","cbCaigUWHbEGoHBE","https://ap.wps.com/l/cbCaigUWHbEGoHBE","pdf",701793,1,12,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n## Models and Dataset Preparation\n# Results and Discussion\n# Conclusions and Future Work","[{\"question\":\"How does the study use stance detection for fake news identification?\",\"answer\":\"It detects misinformation by analyzing how article headlines relate to their corresponding body text and focusing on the stance expressed across them.\"},{\"question\":\"Which datasets and models are used in the experiments?\",\"answer\":\"The study uses the FNC-1 and FARN datasets and evaluates models including logistic regression, XGBoost, and DistilBERT.\"},{\"question\":\"What preprocessing and linguistic features improve performance?\",\"answer\":\"It applies lemmatization, named entity recognition (NER), sentiment analysis, and semantic similarity to capture linguistic and contextual information.\"}]","Enhancing Fake News Detection via Stance Analysis - Leveraging Advanced NLP Techniques and Machine Learning Models | PDF",1785821782,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-fake-news-detection-via-stance-analysis-leveraging-advanced-nlp-techniques-and-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@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/enhancing-fake-news-detection-via-stance-analysis-leveraging-advanced-nlp-techniques-and-machine-learning-models/124353/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study use stance detection for fake news identification?","Question",{"text":75,"@type":76},"It detects misinformation by analyzing how article headlines relate to their corresponding body text and focusing on the stance expressed across them.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets and models are used in the experiments?",{"text":80,"@type":76},"The study uses the FNC-1 and FARN datasets and evaluates models including logistic regression, XGBoost, and DistilBERT.",{"name":82,"@type":73,"acceptedAnswer":83},"What preprocessing and linguistic features improve performance?",{"text":84,"@type":76},"It applies lemmatization, named entity recognition (NER), sentiment analysis, and semantic similarity to capture linguistic and contextual information.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]