# RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation

arXiv: https://arxiv.org/abs/2401.10940 · HTML: https://mlsanity.com/paper/2401.10940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [TI-CNN: Convolutional Neural Networks for Fake News Detection](https://mlsanity.com/paper/1806.00749/) (2018)
- [Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling](https://mlsanity.com/paper/2007.04571/) (2020)
- [Credibility-based Fake News Detection](https://mlsanity.com/paper/1911.00643/) (2019)
- [Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier](https://mlsanity.com/paper/2205.13780/) (2022)
- [CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection](https://mlsanity.com/paper/1809.05252/) (2018)
- [A Model to Measure the Spread Power of Rumors](https://mlsanity.com/paper/2002.07563/) (2020)
- [X-CapsNet For Fake News Detection](https://mlsanity.com/paper/2307.12332/) (2023)

## Built on by (most cited first, 1 of 1)

- [MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers](https://mlsanity.com/paper/2504.21427/) (2025)

## Abstract (arXiv)

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.