Truth in Trees: A Multilayered Linguistic Framework for Automated Fact-Checking with AI-Driven Reasoning

In an era of information overload, distinguishing fact from misinformation is a critical challenge. We introduce AlethaNet, a multilayered linguistic framework for automated fact-checking that integrates syntactic, semantic, and pragmatic analysis with contextual metadata. Our approach combines feature extraction, machine learning (XGBoost), and SHAP-based interpretability with a dynamic credibility scoring system. This score is computed using a weighted formula incorporating historical truthfulness, speaker metadata (e.g., job title, party, platform), and AI-inferred reasoning via DeepSeek R1—a generative LLM shown to approximate expert evaluations on the LIAR dataset. Unlike static, binary classification models, AlethaNet frames the problem as truthful vs. misleading, offering a more nuanced detection of misinformation. Experiments demonstrate improved accuracy (75.65%) when incorporating credibility scores. DeepSeek R1 extends the framework’s ability to assess previously unseen sources, while SHAP ensures transparency by highlighting the most influential features. AlethaNet thus presents a scalable, explainable, and autonomous method for adaptive credibility assessment, advancing AI-driven misinformation detection.