Unified Fake News Detection System (UFNDS) Framework

Abstract

The deliberate spread of misleading or inaccurate material pose as authentic news is known as "fake news." Its increasing prevalence calls for the creation of practical strategies to recognize and counteract its negative effects on people and society. Previous methods of identifying fake news depended on linguistic signals and stylistic components. However, these methods faced limitations in terms of their applicability and accuracy. To overcome these constraints, this study proposes the utilization of an extended stacking ensemble classification algorithm (ES-ECA), a machine learning technique designed specifically for detecting fake news. By employing this innovative approach, we aim to surpass the existing barriers and enhance our ability to combat misinformation. The ensemble classifier outperformed the individual classifiers, with an accuracy of 75.18% and an F1-score of 81.81%. These findings imply that the suggested algorithm can be utilized to lessen the negative effects of fake news on society and is efficient at identifying it. The EHT-DL model leverages a multi-step approach to effectively detect fake news. It begins with preprocessing steps such as text normalization, special character handling, stemming, stop word removal, tokenization, and lemmatization. This ensures the dataset is clean and ready for subsequent processing. Feature extraction is performed using TF-IDF, N-grams, and word embeddings scores to capture semantic information and word importance. After that, the dataset is divided into training and testing sets and the deep learning model Dl4jMlpClassifier is used to classify the data. To tackle the drawbacks of existing techniques, the EHT-DL model incorporates efficient hyperparameter tuning. It uses both Grid Search and Random Search methods to optimize the Dl4jMlpClassifier's hyperparameters. By using this method, the model's accuracy and capacity to distinguish between authentic and fraudulent news are both improved. The effectiveness of the EHT-DL model is shown by the experimental findings. Standard assessment criteria including accuracy, precision, recall, and F1-score are used to assess the model. In terms of accuracy and efficiency, comparisons with current methods demonstrate the superiority of the proposed model in identifying bogus news (83.27% accuracy, 80.62% precision, 71.57% recall, and 75.63% f1-score). To increase classification accuracy and resilience, OE-MDL combines the phases of optimized deep learning (ODL) and optimized machine learning (OML). An optimized Multilayer Perceptron serves as the Meta classifier in the OML phase, on top of base classifiers such as optimized RandomForest, optimized J48, optimized SMO, optimized NaiveBayes, and optimized IBk. The experimental findings show that the OE-MDL algorithm performs better than other methods with the maximum recall (85.18%), accuracy (84.27%), precision (74.17%), and F1-Score (79.29%), providing a practical means of halting the spread of false information. The framework for the Unified Fake News Detection System (UFNDS) is revealed. The final result of the research works and demonstrates how the three main stages—Deep Learning and Optimized Ensemble Machine (OE-MDL), Efficient Hyperparameter-Tuned Deep Learning Model (EHT-DL), and Enhanced Stacking Ensemble Classification Algorithm (ES-ECA)—are cohesively incorporated into the UFNDS framework. We examine the UFNDS framework's architectural design and show how flexible it is to the always changing problems associated with fake news identification.