Distinguishing Truth from Deception: A Machine Learning Approach to Fake News Detection

Abstract: Distribution of fake news on social media is so fast that it endangers public trust and confidence in the political system and society. This paper introduces a powerful machine learning system for the fake news detection that applies the Input-Process-Output (IPO) model for the systematic research procedure. Thus, based on Natural Language Processing (NLP), statistical feature validation, and supervised learning models—Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks—we have successfully built a classification system that is not only accurate but also interpretable and consistent. Words from both fake and verified news sources were processed using sentiment analysis, TF-IDF vectorization, and syntactic feature extraction. Such statistical features as Chi-square tests, T-tests, and Pearson correlation coefficients singled out "Sentiment Polarity Variance" (Feature 100) as the most important one among a number of the features. The SVM model was found to be less effective than the LSTM model as the latter reached 94% for the overall accuracy along with precision (0.93), recall (0.94), and F1-score (0.93). This study strengthens the argument that there is a possibility of a combination of statistical and deep learning being used for the purpose of detection and reducing misinformation and confirming the appearance of safer digital information ecosystems.

Fake news detection, machine learning, LSTM, SVM, natural language processing, misinformation, feature analysis, IPO model, accuracy, statistical validation

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