🚨 92% accuracy sounds impressive until you see what it misses. Excited to share that our researc...🚨 92% accuracy sounds impressive until you see what it misses. Excited to share that our researc...
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🚨 92% accuracy sounds impressive until you see what it misses.
Excited to share that our research paper has been published in the International Journal of Innovations in Science and Technology
š—Ÿš—²š˜ƒš—²š—æš—®š—“š—¶š—»š—“ š——š—²š—²š—½ š—Ÿš—²š—®š—æš—»š—¶š—»š—“ š—®š—»š—± š—”š—Ÿš—£ š—³š—¼š—æ š˜š—µš—² š—œš—±š—²š—»š˜š—¶š—³š—¶š—°š—®š˜š—¶š—¼š—» š—¼š—³ š——š—²š—°š—²š—½š˜š—¶š˜ƒš—² š—¢š—»š—¹š—¶š—»š—² š—–š—¼š—»š˜š—²š—»š˜ We developed and compared multiple ML and NLP models including Logistic Regression, SVM, Bi-LSTM, and BERT for detecting deceptive online content.
Key result: BERT significantly outperformed traditional models, achieving 92.5% accuracy by better capturing contextual meaning in text. šŸ’” But what we learned goes beyond numbers: → High accuracy does not always mean real-world reliability. → Explainability is still a major gap in NLP systems. → Fake news detection is an evolving and continuous problem. This work made one thing clear building a model is easy, but building a reliable system for real-world misinformation is much harder.
Special thanks to my mentors Faheem Ahmed, Ph.D | Kamran Dahri | Muhammad Aquib | Dr Muhammad Yaqoob Koondhar for their guidance and continuous feedback throughout this journey. šŸ”— full paper: https://journal.50sea.com/index.php/IJIST/article/view/1834 Our next goal is to publish in higher-tier, indexed journals and contribute to research that genuinely impacts how we fight misinformation online. Do you think AI alone can solve misinformation, or will humans always be part of the solution? #DataScience #MachineLearning #NLP #DeepLearning #BERT #Research #SiliconValley #TechCommunity
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