Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vincent, Stephin Tom; George, Jossy; Kumar, Pawan Title A name given to the resource Online Fake News Detection using Machine Learning and Natural Language Processing Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WorldSUAS66815.2025.11199266" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WorldSUAS66815.2025.11199266</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022238751?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022238751?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vincent S.T., CHRIST (Deemed to Be University), India; George J., CHRIST (Deemed to Be University), India; Kumar P., CHRIST (Deemed to Be University), India Description An account of the resource Fake news in the digital platforms plays a vital threat to many of the influencing factors of the society. The research focuses on the challenges of online fake news detection from the performance achieved by the machine learning classifiers using the natural processing techniques. Logistic Regression and Random Forest are taken to test the dataset containing labelled fake and real news for the study. The models are evaluated using the key metrices as accuracy, precision, recall, F1-score, confusion matrix and from the ROC AUC score. The research demonstrates which model is more reliable and more consistent for finding the fake and real news. For semantic text analysis BERT embeddings are used in the research as it will help analyze the article with more accuracy and precision. The metrics evaluation and the ROC curve of the both models helps in knowing even the slight deviations projected in the metrics. The differences in the valuations and the metrics values showed the capabilities of both the models in detecting the online news as fake or real. The comparisons made from the two models helps in evaluating the models and to understand the limitations of it as the analyze and detection of the text is more complicated. The research aims to deliver a strong foundation for real-time fake news detection in this new era. 2025 IEEE. Subject The topic of the resource BERT; Fake News Detection; Logistic Regression; Machine Learning; Natural Language Processing; Precision-Recall; Random Forest; ROC AUC; Text Classification Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153925-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online