Machine Learning Approaches For Detecting Fake News: Ensemble Stacking Outperforms Conventional Methods

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NDetect demonstrates superior accuracy in detecting fake news, indicating ensemble methods enhance performance.

NDetect achieves 89% accuracy and 0.957 ROC-AUC score in fake news detection, outperforming traditional models.
Results reveal that stacking-based ensemble learning offers better generalization and performance against individual classifiers.
The model integrates techniques like Logistic Regression and Support Vector Machine to boost predictive accuracy.
Application of ensemble learning approaches could significantly improve misinformation detection on digital platforms.

Jyoti et al. (2025) studied this question.

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View Full Paper

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Authors

NDetect demonstrates superior accuracy in detecting fake news, indicating ensemble methods enhance performance.

NDetect achieves 89% accuracy and 0.957 ROC-AUC score in fake news detection, outperforming traditional models.
Results reveal that stacking-based ensemble learning offers better generalization and performance against individual classifiers.
The model integrates techniques like Logistic Regression and Support Vector Machine to boost predictive accuracy.
Application of ensemble learning approaches could significantly improve misinformation detection on digital platforms.

Jyoti et al. (2025) studied this question.

View Full Paper

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Bookmark