Weighted Ensemble Model for Tackling Fake News

Ananya Kohli, Divyashree Shetti, Sri Lakshmi G N, Vaishnavi Bhat, Shashank Hegde

2025

Abstract

Fake news detection has become crucial in resisting misinformation across multiple domains like social media, news outlets, and public communications. Accurate classification and sentiment analysis play a pivotal role in addressing this challenge. Although traditional machine learning models have shown moderate success, they face limitations in achieving high accuracy and adaptability when applied to diverse types of content. To address this, a fake news detection model is proposed that evaluates the authenticity of news reports by leveraging feature extraction and credibility scoring through accuracy. The proposed study presents a robust fake news detection model that combines BERT (Bidirectional Encoder Representations from Transformers) embeddings with ensemble learning techniques. Eight machine learning classifiers - Logistic Regression, SGD (Stochastic Gradient Descent), XGBoost (Extreme Gradient Boosting), SVM (Support Vector Machine), Random Forest, AdaBoost (Adaptive Boosting), KNN (K-Nearest Neighbor) and Naive Bayes were trained on an 80:20 train-validation split. Using ensemble techniques including Majority Voting, Unweighted Averaging and Weighted Averaging, the proposed work with Weighted Averaging proved to be the most accurate method, with an accuracy of 94.8317%. This is because the weights were normalized depending on the individual model approach, making the model a reliable and adaptable solution to misinformation detection.

Paper Citation

in Harvard Style

Kohli A., Shetti D., Lakshmi G N S., Bhat V. and Hegde S. (2025). Weighted Ensemble Model for Tackling Fake News. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 878-884. DOI: 10.5220/0013606400004664

in Bibtex Style

@conference{incoft25,
author={Ananya Kohli and Divyashree Shetti and Sri Lakshmi G N and Vaishnavi Bhat and Shashank Hegde},
title={Weighted Ensemble Model for Tackling Fake News},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={878-884},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013606400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Weighted Ensemble Model for Tackling Fake News
SN - 978-989-758-763-4
AU - Kohli A.
AU - Shetti D.
AU - Lakshmi G N S.
AU - Bhat V.
AU - Hegde S.
PY - 2025
SP - 878
EP - 884
DO - 10.5220/0013606400004664
PB - SciTePress