RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation
Abstract
In the era of information proliferation, discerning the credibility of news content poses an ever-growing challenge. This paper introduces RELIANCE, a pioneering ensemble learning system designed for robust information and fake news credibility evaluation. Comprising five diverse base models, including Support Vector Machine (SVM), naive Bayes, logistic regression, random forest, and Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs an innovative approach to integrate their strengths, harnessing the collective intelligence of the ensemble for enhanced accuracy. Experiments demonstrate the superiority of RELIANCE over individual models, indicating its efficacy in distinguishing between credible and non-credible information sources. RELIANCE, also surpasses baseline models in information and news credibility assessment, establishing itself as an effective solution for evaluating the reliability of information sources.
Cite
@article{arxiv.2401.10940,
title = {RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation},
author = {Majid Ramezani and Hamed Mohammadshahi and Mahshid Daliry and Soroor Rahmani and Amir-Hosein Asghari},
journal= {arXiv preprint arXiv:2401.10940},
year = {2024}
}
Comments
Published in: 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP) Publisher: IEEE Conference Location: Babol, Iran Date of Conference: 21-22 February 2024 Date Added to IEEE Xplore: 25 March 2024 pages={1-9}, https://ieeexplore.ieee.org/document/10475305