AN UNSUPERVISED LEARNING APPROACH FOR REAL-TIME GENERATION AND DETECTION OF TEXT-BASED FAKE NEWS DETECTION

Abstract

The rapid spread of fake news has become a critical issue in the digital era, posing challenges to information authenticity and decision-making. This research presents a novel method for fake news detection leveraging a Generative Adversarial Network (GAN) combined with the BLEU (Bilingual Evaluation Understudy) score for evaluating textual quality. The proposed model uses a GAN framework to generate synthetic news data and trains a classifier to distinguish between genuine and fabricated articles. The BLEU score, commonly used in machine translation, is adapted to assess the accuracy of generated text against real-world news. Experimental results show the effectiveness of this approach, with detection performance categorized based on BLEU score ranges: Excellent (0.7+), Good (0.5-0.7), Average (0.3-0.5), and Poor (<0.3). With a BLEU score of 0.66, the model demonstrates strong performance in distinguishing fake from real news, with potential applications in automated content moderation and misinformation detection.