内容调研与材料核查 · The FACTS framework — verification as craft, sourcing as strategy

A piece of content is a stack of claims wearing a voice. This framework verifies the stack before it ships — proportionately (color gets a glance; load-bearing claims get the full chain). Most published errors trace to a skipped F (nobody listed the claims) or a skipped T (an AI citation trusted because the link worked).

F — Find the load-bearing claims

Inventory the draft's claims and triage: load-bearing (the piece stands on them — the headline stat, the causal claim, the quote) vs color (illustrative, low-stakes). Load-bearing claims get the full FACTS treatment; color gets a sanity check. This proportionality is what makes rigor sustainable at social-media cadence — verify everything equally and you'll verify nothing consistently.

A — Ascend to the primary source

Trace each load-bearing claim UP the chain: the marketing blog cites the news article cites the report — cite the report. Aggregators are leads, not sources. At the primary, check: who ran it, sample and method, who funded it (a vendor study is attributed as a vendor study), and what the finding actually says (abstracts and headlines routinely overstate). Citing the primary is also strategy: most competitors quote the aggregator; the account that reads the source becomes the source.

C — Check freshness + context

Three decay modes kill true-once claims: staleness (a 2015 figure quoted as current), supersession (later research reversed it) and retraction (known-false, however popular). Plus the zombie-stat tell: citations that circle blogs forever without reaching a named study. Context check: does the stat mean what the draft implies (Mehrabian-class misapplication)? Scope check: does the sample generalize to the claim's subject? Date-stamp every stat in the source log so future reuse re-checks age.

T — Test AI output hard

AI-supplied citations are guilty until verified — the 2026 evidence (60%+ wrong news citations; 12-fold fabrication surge; 5-of-45 corporate reports) makes this structural, not paranoid. The protocol per citation: (1) the source exists — search the title/authors independently; (2) the link is honest — a working DOI can resolve to an unrelated paper (identifier hijacking); (3) the source says what's claimed — open it and find the claim. Anything failing any step gets replaced or cut. AI is excellent at drafting and at summarizing sources you hand it — the failure mode is asking it to supply the sources.

S — Show your sources

Attribution that survives social formats: in-line naming where the claim appears ("per the 2026 Lancet audit"), in-graphic credit on quote cards and infographics (source + date in the design — route to those skills' rules), links in caption/comments per platform norms. Keep a source log per piece (claim → source → date → link) — it makes corrections fast, reuse safe, and audits painless. Quoting ethics: short quotes with attribution, never wholesale lifting, never fabricated or context-stripped quotes (→ quote-cards-and-text-graphics). YMYL claims (health/money) carry the heightened bar: official/peer- reviewed primaries, qualified language, advice disclaimers. Then the human signs off and WoopSocial publishes — verification happens before scheduling, never after.