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This paper presents TruthLens, a hybrid context aware and evidence-based fact checking system designed to detect misinformation in online news and social media content. With the rapid growth of digital platforms, misinformation spreads quickly, creating challenges for individuals and society . Traditional detection systems rely mainly on static datasets and fail to adapt to real-time scenarios. The proposed system integrates transformerbased deep learning models such as BERT for contextual understanding and RoBERTa-based Natural Language Inference (NLI) for reasoning over retrieved evidence. Unlike conventional approaches, TruthLens incorporates source-aware analysis and real-time evidence retrieval to improve prediction reliability. The system supports multiple input formats including text, URLs, and images using OCR techniques, and also includes multilingual capabilities through translation mechanisms. It generates explainable outputs with confidence scores, enhancing transparency and user trust. Experimental results show that transformerbased models outperform traditional methods, achieving high accuracy and improved real-world applicability.
Keywords:
Misinformation Detection, BERT, RoBERTa, Natural Language Inference, Deep Learning, Fake News Detection.
Cite Article:
"TruthLens: Hybrid Context Aware and Evidence-Based Fact Checking System for Online News", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b109-b113, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604152.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator