From Claims to Context: Holistic Information Verification Requires Contextual Signals

Generative AI has dramatically lowered the cost of producing content that is internally coherent, semantically plausible, and stylistically authentic, thus eroding the discriminative signals on which automated fact-checking has historically relied. We argue that the field's claim-centric default is increasingly mis-calibrated for modern online environments. The context surrounding a claim and its retrieved evidence which includes their temporal grounding, source attribution, and position within web topology, carries information that content alone does not. Therefore, this paper argues that information verification systems must consider both the claim itself and necessary contextual signals during the verification process. We develop this position along four arguments: first, that (1) machine learning methods should consider the inherent temporal nature of information verification, a process limited by available evidence at the moment of verification. We then argue that contextual signals are useful, (2) broadly, for information verification; (3) particularly, in cases where retrieved evidence supports conflicting judgments of the claim; and (4) for detecting varying degrees of verifiability, ambiguity, and subjectivity in a claim. We also show that the latter manifests in varied forms and overall considerable proportions in a range of widely-used benchmarks. We outline considerations for designing and evaluating holistic information verification systems that integrate these aspects, and hope this paper sparks broader discussion within this field.