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 Articles Article Faculty Publications -Articles 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 Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach Subject The topic of the resource Convolutional Neural Networks; Machine learning; TF-IDF Weighted Vector; Word2Vec Description An account of the resource Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Nae Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset. 2023, Ismail Saritas. All rights reserved. Creator An entity primarily responsible for making the resource Khan Z.A.; Rekha V. Source A related resource from which the described resource is derived International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 3, pp. 1065-1076. Publisher An entity responsible for making the resource available Ismail Saritas Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174857890&amp;partnerID=40&amp;md5=fffe94079b40d894912d13c37d245571" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174857890&amp;partnerID=40&amp;md5=fffe94079b40d894912d13c37d245571</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21476799 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article 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., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Rekha V., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India