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 Aman, Arnisha; Sundaram, Divya; Bhavesh, K.; Anthony, Madalaimuthu Title A name given to the resource Prediction of Grandiose Narcissism Using Machine Learning 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;1288 LNNS;pp.249-257 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3102-5_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3102-5_18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010812465?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010812465?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 Aman A., Christ University, Bengaluru, India; Sundaram D., Christ University, Bengaluru, India; Bhavesh K., Christ University, Bengaluru, India; Anthony M., Christ University, Bengaluru, India Description An account of the resource The Gen-Zs have the tendency to exhibit a sense of self-importance and superiority excessively over social media. This study intends to predict Grandiose Narcissism based on Instagram usage and Fear of Missing Out (FoMO) among young adults. The study was conducted on a sample size of 300 young adults, recruited using convenient sampling, residing in the state of Assam, India. This study employed various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNNs), and Gaussian Naive Bayes, to analyse the predictors of Grandiose Narcissism. Results showed that machine learning algorithms, especially KNN (90.7%) and Random Forest (88.70%) predicted Grandiose Narcissism accurately based onFoMO, Self-Esteem, PAUM. Additionally, Area Under Curve (AUC) in the range of 0.850.91 indicated that the variables in the data set are being discriminated in the context of specificity and sensitivity thoroughly. Significant influence of grandiose narcissism and FoMO on Instagram usage highlighted the role of social validation in enhancing online engagement. Future studies can include these algorithms to deduce patterns and develop real timebots to provide psychologically safe online environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Grandiose narcissism; Instagram usage; Machine learning; Social media Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981963101-8; 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