بهبود شناسایی اخبار جعلی با استفاده از روش‌های یادگیری ماشین

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه سجاد، مشهد، ایران

2 دانشگاه صنعتی سجاد - عضو هیئت علمی

چکیده

گسترش سریع اخبار جعلی در پلتفرم‌های دیجیتال به چالشی جدی در جوامع امروزی تبدیل شده و بر افکار عمومی، ثبات سیاسی و اعتماد اجتماعی تأثیرگذار است. با وجود پژوهش‌های گسترده، بسیاری از روش‌های موجود در تشخیص اخبار جعلی از ضعف در تعمیم‌پذیری، مقاومت پایین در برابر متون کوتاه و نویزی، و وابستگی به ویژگی‌های مهندسی‌شده دستی رنج می‌برند. در این پژوهش، یک چارچوب ترکیبی نوین برای تشخیص اخبار جعلی ارائه می‌شود که انتخاب ویژگی مبتنی بر مجموعه‌های فازی–تقریبی کرنلی (KFRS) را با یک سامانهٔ تجمعی دومرحله‌ای متشکل از مدل‌های یادگیری عمیق و یادگیری ماشین کلاسیک تلفیق می‌کند. در این چارچوب، شبکه‌های LSTM و Bi-LSTM در کنار رگرسیون لجستیک، ماشین بردار پشتیبان و XGBoost به‌کار گرفته شده و از راهبردهای رأی‌دهی نرم و Stacking برای بهره‌گیری از قابلیت‌های مکمل آن‌ها استفاده می‌شود. مؤلفهٔ KFRS با مدیریت عدم‌قطعیت و کاهش نویز، نقش مؤثری در بهبود بازنمایی ویژگی‌ها، به‌ویژه برای متون کوتاه، ایفا می‌کند. همچنین،嵌ه‌سازی واژگانی Skip-Gram به‌منظور استخراج روابط معنایی میان واژگان مورد استفاده قرار گرفته است. ارزیابی چارچوب پیشنهادی بر روی سه مجموعه‌دادهٔ معیار LIAR، FakeNewsNet و FakeEdited نشان‌دهندهٔ عملکرد پایدار و بهبود قابل‌توجه معیارهای Recall و F1-score است. نتایج حاصل بیانگر کارایی، پایداری و قابلیت کاربرد عملی روش پیشنهادی در تشخیص اخبار جعلی در دنیای واقعی است.

کلیدواژه‌ها

عنوان مقاله [English]

Improving Fake News Detection Using Machine Learning Methods

نویسندگان [English]

  • Fatemeh Shahidi 1
  • Javad Hamidzadeh 2
1 Faculty of Computer Engineering and Information Technology, Sadjad University, Mashhad, Iran
2 Computer engineering, Faculty of Computer Engineering and Information Technology, Sadjad University of Technology, Mashhad, Iran
چکیده [English]

The rapid spread of fake news on digital platforms has become a major societal challenge, influencing public opinion, political stability, and social trust. Despite extensive research, existing detection models often suffer from limited generalization, weak robustness to noisy or short texts, and reliance on manually engineered features. Moreover, the evolving linguistic and contextual nature of fake news reduces the long-term effectiveness of conventional approaches. To address these limitations, this study proposes a novel hybrid fake news detection framework that, for the first time, integrates Kernel Fuzzy Rough Set (KFRS)–based feature selection with a two-stage ensemble of deep learning and traditional machine learning classifiers. Unlike prior studies that rely solely on either neural or statistical models, the proposed approach combines LSTM and Bi-LSTM networks with Logistic Regression, Support Vector Machine, and XGBoost, leveraging their complementary strengths through soft voting and stacking. The KFRS component plays a critical role in refining feature representations by handling uncertainty and reducing noise, which is particularly beneficial for short and noisy textual data. Skip-Gram word embeddings are employed to capture semantic relationships between words and enhance contextual understanding. The proposed framework is evaluated on three benchmark datasets—LIAR, FakeNewsNet, and FakeEdited—demonstrating consistently strong performance across diverse domains. Ablation experiments further confirm that incorporating KFRS leads to substantial improvements in Recall and F1-score. Overall, the proposed method offers a robust, scalable, and practically deployable solution for real-world fake news detection.

کلیدواژه‌ها [English]

  • Fake news detection
  • deep learning
  • machine learning
  • fuzzy rough sets
  • ensemble learning

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