  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.
