A high-signal item supported by a public source with strong relevance, freshness, and evidence. It does not mean VQV independently verified the claim.
Glossary
How to read VQV
Plain-language definitions for the labels, scores, pages, and transparency signals used across VQV.me.
VQV is a public briefing system. These terms explain how the site organizes signals; they are not claims of independent fact-checking.
A signal that appears to be gaining momentum through recency, score, source relevance, or repeated activity inside the tracked topic.
A fresh item from the latest scan or recent source window that is relevant enough to surface as a current signal.
An item worth monitoring, but not strong enough to treat as a major source-backed signal yet.
A weak or loosely related match. These items are usually down-ranked and kept out of primary reading surfaces.
A likely irrelevant, repeated, vague, or weakly related match. Noise helps VQV explain what it is choosing not to promote.
A public score reflecting source quality, topic relevance, freshness, evidence, and ranking inputs. It is not a fact-checking score.
The internal ranking score shown publicly on cards. It helps sort signals within a topic and across reading pages.
The number of times readers clicked Copy VQV Link for a signal. It does not use accounts, cookies, or personal tracking.
A deterministic explanation generated from actual ranking inputs such as source quality, freshness, signal strength, reposts, and low-noise scoring.
A compact history block showing latest run, 24-hour, 7-day, and 30-day topic activity when historical data is available.
A compact view of the public sources contributing to the latest topic brief or signal set.
A public summary of which sources are carrying useful VQV signal, based on activity, source-backed counts, reposts, and source health.
A topic quality metric estimating how much weak or noisy material appears in recent findings.
A topic quality metric estimating repeated URLs or repeated story overlap in recent findings.
A deterministic topic-level quality layer using freshness, source diversity, evidence, top signal strength, and low-noise scoring.
Static editorial entry points such as AI Coding or AI Infrastructure that group related tracked topics into broader reading paths.
Static JSON files that expose safe public VQV reading data. They do not include logs, secrets, credentials, raw DB internals, or personal data.