An Interpretable Parameter Tuning Strategy for Machine Learning-Based Fake News Detection

Abstract

Automation of fake news detection has become a significant research challenge due to the growing spread of false information through digital media. While machine learning models have shown encouraging results in the categorization of textual false news, their efficacy is heavily reliant on the choice of suitable parameters. Current research frequently uses parameter tuning methods directly without methodically examining the underlying performance constraints of baseline models, which could lead to ineffective parameter search space exploration. A methodology for parameter adjustment for machine learning-based fake news detection is proposed in this paper. The approach draws a connection between model shortcomings and pertinent parameter configurations by examining the behavior of four baseline classifiers: Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine. Following that, Random Search is used to quickly assess chosen parameter combinations and find appropriate configurations for every classifier. The experimental study shows that different machine learning models need different parameter choices in order to achieve effective generalization and consistent performance. By establishing tuned baseline models for further ensemble learning and enhancing our understanding of how parameter configurations affect model behavior, the suggested framework advances the field of fake news detection research. The study offers a comprehensible and computationally efficient basis for creating reliable machine learning-based false news identification systems.