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Rezanejad, Amin, Seyed Danesh, Amir. (1405). Truth Is All You Need: Enhancing Fake News Detection with Interpretable Language Models. فیزیولوژی و بیوتکنولوژی آبزیان, (), -. doi: 10.22124/cse.2026.33066.1162
Amin Rezanejad; Amir Seyed Danesh. "Truth Is All You Need: Enhancing Fake News Detection with Interpretable Language Models". فیزیولوژی و بیوتکنولوژی آبزیان, , , 1405, -. doi: 10.22124/cse.2026.33066.1162
Rezanejad, Amin, Seyed Danesh, Amir. (1405). 'Truth Is All You Need: Enhancing Fake News Detection with Interpretable Language Models', فیزیولوژی و بیوتکنولوژی آبزیان, (), pp. -. doi: 10.22124/cse.2026.33066.1162
Rezanejad, Amin, Seyed Danesh, Amir. Truth Is All You Need: Enhancing Fake News Detection with Interpretable Language Models. فیزیولوژی و بیوتکنولوژی آبزیان, 1405; (): -. doi: 10.22124/cse.2026.33066.1162

Truth Is All You Need: Enhancing Fake News Detection with Interpretable Language Models

Computational Sciences and Engineering
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 اردیبهشت 1405
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22124/cse.2026.33066.1162
نویسندگان
Amin Rezanejad1؛ Amir Seyed Danesh* 2
1Department of Computer Engineering, University of Guilan,Rasht, Iran.
2Amir Seyed Danesh Faculty of Technology and Engineering, East of Guilan, University of Guilan, Rudsar-Vajargah, Iran.
چکیده
The rapid spread of fake news on digital platforms poses a significant threat to informed public discourse and societal trust. While Large Language Models (LLMs) like BERT have shown remarkable accuracy in automated fake news detection, their opaque nature hinders user trust and understanding. This paper presents a framework that combines the high predictive performance of BERT with post-hoc interpretability techniques to enhance both the effectiveness and transparency of fake news detection systems. Specifically, we fine-tune BERT for binary fake news classification on the COVID-19 Fake News Dataset and employ Local Interpretable Model-agnostic Explanations (LIME) and BERT attention visualization to elucidate the model's decision-making process. Our results demonstrate that the fine-tuned BERT model achieves excellent performance, with an accuracy of 97.66% and an F1-score of 97.49% on the test set. Furthermore, LIME explanations highlight the contribution of specific words to individual predictions, while attention visualizations reveal which token relationships the model deems important. This integrated approach underscores that "truth" in machine prediction encompasses not only high accuracy but also explainability, thereby fostering greater confidence in automated fake news detection systems.
کلیدواژه‌ها
Data Mining؛ Text Mining؛ Fake news؛ Natural Language Processing؛ Large Language Models؛ Interpretable Artificial Intelligence
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تعداد مشاهده مقاله: 2


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