Fake News Detection on Social Media Using Ensemble Methods

In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread misinformation and potentially harmful consequences. Despite numerous methodologies proposed for fake news detection, including knowledge-based, language-based, and machine-learning approaches, their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or inconsistencies. Our study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news detection. We employ multiple feature extraction methods, including Count Vectorizer, Bag of Words, Global Vectors for Word Representation (GloVe), Word to Vector (Word2Vec), and Term Frequency-Inverse Document Frequency (TF-IDF), alongside feature selection techniques such as Information Gain, Chi-Square, Principal Component Analysis (PCA), and Document Frequency. This comprehensive approach enhances the model's ability to identify and analyze relevant features, leading to more accurate and effective fake news detection. Our findings highlight the importance of a multi-faceted approach, offering a significant improvement in model accuracy and reliability. Moreover, the study emphasizes the adaptability of the proposed ensemble model across diverse datasets, reinforcing its potential for broader application in real-world scenarios. We introduce a pioneering ensemble technique that leverages both machine-learning and deep-learning classifiers. To identify the optimal ensemble configuration, we systematically tested various combinations. Experimental evaluations conducted on three diverse datasets related to fake news demonstrate the exceptional performance of our proposed ensemble model. Achieving remarkable accuracy levels of 97%, 99%, and 98% on Dataset 1, Dataset 2, and Dataset 3, respectively, our approach showcases robustness and effectiveness in discerning fake news amidst the complexities of contemporary information landscapes. This research contributes to the advancement of fake news detection methodologies and underscores the significance of integrating feature extraction and feature selection strategies for enhanced performance, especially in the context of intricate, high-dimensional datasets.