Prediction of Grandiose Narcissism Using Machine Learning

Title

Prediction of Grandiose Narcissism Using Machine Learning

Creator

Aman, Arnisha; Sundaram, Divya; Bhavesh, K.; Anthony, Madalaimuthu

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1288 LNNS;pp.249-257

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Grandiose narcissism; Instagram usage; Machine learning; Social media

Coverage

Aman A., Christ University, Bengaluru, India; Sundaram D., Christ University, Bengaluru, India; Bhavesh K., Christ University, Bengaluru, India; Anthony M., Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981963101-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-3102-5_18

https://www.scopus.com/pages/publications/105010812465?origin=resultslist