DYNAMIC APPROCH FOR FAKE NEWS DETECTION USING BERT
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In today’s fast-paced digital world, fake news spreads quickly and can cause real harm. Our project aims to tackle this issue by developing a system that detects fake news on social media. We combine advanced machine learning techniques with Natural Language Inference (NLI) to analyze and classify news articles. Using data from Politick, a trusted fact-checking source, our system can identify whether a statement is true, false, or somewhere in between (like "half-true" or "pants-fire"). At the heart of our approach are powerful deep-learning models like BERT and SBERT, which understand the subtle meanings in text. We evaluate the system’s accuracy using key measures such as precision, recall, F1-score, and ROC curves. To make this tool useful in real life, we built a dashboard that allows users to check news in real time and receive alerts about potential misinformation. Our research shows that machine learning can significantly improve the accuracy of identifying fake news. However, as misinformation continues to evolve, it is essential to keep refining and improving these detection systems to stay ahead.
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Dahiphale, V. V. (2026). Dynamic Approch for Fake News Detection using Bert. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.281
Dahiphale, Vyankati. "Dynamic Approch for Fake News Detection using Bert." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.281.
Dahiphale, Vyankati. "Dynamic Approch for Fake News Detection using Bert." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.281.
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