Explainable Early-Warning of High-Amplification Crisis-Related Disinformation in Social Media Streams

Abstract

Rumors, misleading claims, and other factually risky information units may gain visibility during crises before verification processes are complete. Existing detection systems often address factual status or diffusion separately, whereas operational monitoring requires identifying units that are both factually risky and highly amplified by a forecast horizon. This paper proposes the Disinformation Diffusion Virality Risk Index (DDVRI), an explainable early-warning framework for estimating this joint risk from evidence observed during a prespecified early window. The formulation defines early histories, adjudicated factual labels, fold-specific amplification thresholds estimated without test outcomes, optional absolute amplification floors, block-structured early representations, calibrated risk scores, and capacity-constrained alerting rules. The proposed framework is evaluated using two open datasets, one focused on crisis rumors and another focused on COVID-19 health misinformation with social engagement information. The results support DDVRI as a probabilistic early-warning and prioritization methodology for rare cases that combine factual risk and high-amplification, rather than as a general fake-news classifier or an automatic moderation system.