VERITAS: A Neuro-Symbolic Approach to Quantifying Epistemic Divergence and Harm Potential in Online Health Narratives

Objective: Online health information seeking is rising, and individuals increasingly act on peer advice without clinical oversight, adjusting doses, delaying care, and modifying treatment. Current misinformation detection assumes factually inaccurate content is what makes these decisions unsafe. We introduce VERITAS (Verification Engine for Risk-aware Information Trust Assessment in health Stories) and formalize the Risk Irrelevance Principle: divergence from accepted clinical practice and potential for harm are distinct, weakly associated dimensions that must be assessed separately. Materials and Methods: VERITAS transforms unstructured health narratives into Agent-Action-Outcome graphs and computes two continuous metrics: Narrative Truth Distance (NTD), quantifying epistemic divergence, and Narrative Risk Score (NRS), assessing harm potential. We evaluated VERITAS on 704 threads from four Reddit health communities. Two domain experts annotated 2,000 segments (Krippendorffs =0.78-0.81). NTD-NRS independence was validated using seven tests. Results: NTD and NRS shared under 5% of variance (r = 0.222; mutual information 0.096 bits): a posts divergence from consensus conveys little about whether acting on it will cause harm. On 435 labeled posts, VERITAS identified 62.2% of expert-labeled misinformation versus 57.5% for the strongest text classifier, the gain concentrated in factually plausible content describing unsafe self-management (27.6% of misinformation) that accuracy-focused classifiers approve. VERITAS assessed 37.8% of this misinformation as low-risk, pending clinical validation. Discussion: Fact-checking-based screening systematically approves the content most likely to prompt unsafe self-management while flagging content least likely to cause harm. Conclusion: Separating divergence from harm potential shifts verification from whether information is correct to whether it is safe to act upon.