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 Khan, Zahir Abbas; Rekha, V. Title A name given to the resource Optimizing Fake News Classification Using Data Fusion and NLP-Based Machine Learning Techniques 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 Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.315-326 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010596080?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010596080?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 Khan Z.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India; Rekha V., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India Description An account of the resource In this research, the performance of different machine learning algorithms for identifying fake news using a dataset of news articles labeled as fake or real. The dataset was preprocessed to remove stop words, punctuation, digits, and special characters, and text normalization was applied. Two feature extraction methods, BOW (Bag-of-Words) and TF-IDF, were utilized to convert text data into numerical features. The dataset was split into training and testing phases to train and evaluate models, including Support Vector Classifier, Logistic Regression, Decision Trees, Gradient Boosting Classifier, Random Forest, and Multinomial Naive Bayes. Ensemble models combining various classifiers were also tested. Performance metrics, including precision, recall, and F1-score, were assessed, and confusion matrices were analyzed. Results showed that TF-IDF generally outperformed BOW. The Random Forest model achieved the highest precision (93%) but had a lower recall (83%). The SVC model showed a balanced performance with a precision of 90%, recall of 87%, and an F1-score of 86%. Ensemble models like GB?+?RF exhibited high precision (99%) but lower recall. These findings highlight the strengths of different algorithms in fake news detection and inform the development of practical classification tools. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Count vectorizer; Fake news detection; Machine learning algorithms; Natural language processing (NLP); Random forest classifier; Support vector classifier Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; 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