Detecting AI-Generated Fake News Using Multi-Layer Perception Classifier
DOI:
https://doi.org/10.63665/IJAICE.0203.05Keywords:
AI-Generated Fake News, Fake News Detection, Multi-Layer Perceptron (MLP), Natural Language Processing (NLP), GPT-4, Deep Learning, Text Classification, Misinformation Detection, Machine Learning, Information IntegrityAbstract
The rapid advancement of large language models (LLMs), such as GPT, has increased the generation and spread of AI-created fake news, creating significant challenges for reliable information dissemination. Conventional text-classification techniques often have limited ability to distinguish authentic news from artificially generated or manipulated content. To address this challenge, this study proposes a Multi-Layer Perceptron (MLP) classifier integrated with Natural Language Processing (NLP) techniques for the effective detection of AI-generated fake news. The textual data undergoes preprocessing steps, including tokenization, stop-word removal, and vectorization, to extract meaningful linguistic features. These features are subsequently provided to the MLP model, where multiple hidden layers and nonlinear activation functions learn complex patterns associated with fabricated news content. A dedicated dataset consisting of AI-generated news samples created using GPT-4 across 42 different news categories was developed for training and evaluation. Experimental results indicate that the proposed MLP-based approach achieves high classification accuracy and strong F1-score performance, outperforming several traditional machine-learning methods. The study demonstrates the effectiveness of combining NLP-based feature extraction with deep learning for identifying AI-generated misinformation and contributes toward improving the reliability and integrity of online information environments.
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