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.

翻译：在信息爆炸的时代，辨别新闻内容的可信度已成为日益严峻的挑战。本文提出RELIANCE，一种用于鲁棒性信息与虚假新闻可信度评估的开创性集成学习系统。该系统由五种多样化的基模型组成，包括支持向量机(SVM)、朴素贝叶斯、逻辑回归、随机森林以及双向长短期记忆网络(BiLSTMs)，并采用创新方法整合各模型优势，通过汇集集成模型的集体智慧提升准确率。实验证明，RELIANCE相较于单一模型具有优越性，在区分可信与不可信信息来源方面效果显著。此外，RELIANCE在信息与新闻可信度评估中超越基线模型，成为评估信息来源可靠性的有效解决方案。