Spot AI Writing: Wikipedia’s Checklist for Editors

A weird thing has happened over the last year: “AI writing detection” has started to feel like spam detection circa 2005. Not because it’s identical, but because the incentives line up the same way—cheap content scales, moderation budgets don’t, and trust becomes the scarce resource.

That’s why Wikipedia’s surprisingly practical page on “Signs of AI writing” matters. Wikipedia isn’t trying to win a parlor game (“human or bot?”). It’s trying to protect a system where verification is the product. If you work in media and entertainment—or anywhere that depends on audience trust—this is a useful mindset shift.

This post reframes Wikipedia’s guidance as a case study for the AI in Defense & National Security series: how teams can spot LLM-generated prose, what those signals mean in high-stakes information environments, and how to turn “detection” into something more actionable—behavior analysis, provenance, and personalization that doesn’t compromise integrity.

Wikipedia’s core insight: detect patterns, not “robot vibes”

Wikipedia’s best contribution is simple: stop looking for a single tell. Look for clusters of signals—language, structure, sourcing behavior, and editorial fingerprints. That’s how experienced Wikipedia editors handle questionable contributions at scale.

The most useful “signs of AI writing” (and what they look like)

Wikipedia’s guide emphasizes characteristics that show up when text is produced by predicting likely next words rather than reporting or reasoning from firsthand knowledge. In practice, these are the tells that actually hold up under review:

• High fluency with low information density: paragraphs that read smoothly but add few verifiable facts.
• Over-generalized claims: sweeping statements without dates, locations, quantities, or named entities.
• Repetitive structure: the same sentence rhythm repeated across sections (topic sentence → generic elaboration → vague wrap-up).
• Cautious, non-committal wording: lots of “often,” “various,” “many experts,” without specifying who/where/when.
• Odd specificity in the wrong places: extremely precise-sounding phrases paired with missing fundamentals (no sources, no primary details).
• Citation mismatches (for Wikipedia this is huge): references that don’t support the claim, or “citation-shaped” text that feels bolted on.

Here’s the stance I agree with: fluency is no longer evidence of credibility. In 2025, fluency is table stakes—and also cheap.

Why this matters beyond Wikipedia

Media and entertainment teams are now dealing with the same class of problem: content that sounds right spreads faster than content that is right. And when AI can generate 1,000 versions of the same narrative, the threat isn’t one fake article—it’s behavioral saturation.

Defense and national security organizations have lived with this longer than most sectors: influence operations, synthetic personas, and information laundering. The difference now is volume and speed. LLMs compress the cost of producing persuasive text down to nearly zero.

A modern trust strategy can’t rely on “editorial gut feel.” It needs repeatable checks and observable signals.

The real risk: AI writing isn’t just “fake”—it’s scalable ambiguity

Most companies get this wrong: they frame AI writing as a binary—human or machine, real or fake. Wikipedia’s approach hints at a better framing.

The risk isn’t that AI text is always false. The risk is that it’s often unaccountable: unclear authorship, unclear sourcing, unclear incentives, and unclear editorial responsibility.

Media & entertainment: where trust meets monetization

In media and entertainment, AI writing shows up everywhere:

• automated entertainment explainers and recaps
• synthetic “news” about celebrities, releases, and box office
• fake interviews, quotes, and behind-the-scenes “leaks”
• fan-wiki edits and lore summaries that get scraped into recommendation systems

If that content gets distributed, you’re not just risking a correction—you’re risking:

• audience churn (“this outlet feels spammy now”)
• brand safety issues (advertisers don’t want to sit next to synthetic sludge)
• recommendation decay (bad inputs poison personalization)

Defense & national security: verification under adversarial pressure

In national security contexts, “AI-generated prose” becomes more than a quality issue. It becomes a signal manipulation problem—adversaries flooding channels with plausible narratives, eroding confidence, and forcing analysts to spend time disproving claims instead of producing insight.

Wikipedia’s checklist is valuable here because it’s built for an adversarial environment: open editing, constant pressure, and a huge surface area.

A practical workflow: using Wikipedia-style checks in your newsroom or studio

The goal isn’t to play detective. The goal is to reduce the cost of verification while increasing confidence.

Step 1: Start with the “information density” test

Answer-first: If a paragraph can’t be converted into 2–3 checkable bullet points, it’s suspect.

Try extracting:

• Who did what?
• When and where?
• What’s the primary evidence?

If you get mostly adjectives and vibes—“critically acclaimed,” “widely regarded,” “sparked conversation”—you’re looking at low-density prose. That’s a common LLM footprint and a common content-farm footprint.

Step 2: Run a “source alignment” check (even if you’re not Wikipedia)

Wikipedia’s ecosystem trains editors to ask: Do the sources actually support the claim?

In media workflows, adapt this into a lightweight internal rule:

1. Highlight the three strongest factual claims in the piece.
2. Require one supporting artifact per claim: a transcript line, document, direct quote, dataset, or confirmed reporting.
3. If artifacts don’t exist, the claims get rewritten as clearly labeled opinion—or removed.

This is where AI writing often collapses: it can fabricate plausible-sounding statements faster than it can provide verifiable backing.

Step 3: Look for “template voice” across your content inventory

Answer-first: One AI-written article is manageable; a library of AI-written articles creates a detectable house style that audiences learn to distrust.

Run periodic audits for:

• repeated openings (“X has been making waves…”)
• repeated transitions and section structures
• identical phrasing across unrelated topics

This is also where audience behavior analysis comes in. If engagement is high but:

• time-on-page is low,
• scroll depth is shallow,
• return visits drop,

…you may be watching curiosity clicks rather than real trust.

Step 4: Require “editorial fingerprints” for high-impact content

Wikipedia has talk pages, edit histories, and norms that create accountability. Your org needs equivalents.

For sensitive categories (politics-adjacent entertainment, conflict reporting, celebrity allegations, security incidents), require:

• named editor review
• a change log (“what changed since draft 1?”)
• a short provenance note internally: why we believe this is true

This doesn’t need to be public-facing to be effective. It needs to exist so your team can defend decisions later.

Detection alone won’t save you—pair it with provenance and personalization

Answer-first: The winning strategy is not “catch all AI text.” It’s “rank content by trust signals and distribute accordingly.”

This aligns directly with media platforms’ distribution reality: personalization systems decide what gets seen. If the system can’t tell the difference between trustworthy reporting and synthetic filler, it will optimize for whatever drives cheap engagement.

Provenance signals that scale better than style policing

Style-based detection is brittle. Provenance-based governance holds up.

Consider adding structured signals such as:

• origin: human-authored, AI-assisted, AI-generated (with policy definitions)
• evidence type: firsthand reporting, primary document, secondary aggregation
• verification level: unverified, reviewed, fact-checked
• update cadence: last verified timestamp

Even if you never show this metadata to users, your internal systems can use it to:

• gate distribution,
• route items for review,
• avoid recommending low-verification content into sensitive contexts.

Personalization without poisoning trust

If you’re chasing leads (subscriptions, registrations, advertiser trust), the KPI that matters isn’t raw clicks—it’s trusted attention.

A practical approach I’ve seen work:

• Use high-trust content to build long-term profiles (what users return to).
• Use lower-trust content carefully: limit frequency, avoid sensitive topics, don’t let it define the user’s interests.
• Build “trust-aware” recommendation rules: if a user is reading about conflict, elections, or public safety, restrict suggestions to higher verification tiers.

That’s a media tactic that maps cleanly to defense analytics: context-aware triage beats blanket filtering.

Quick Q&A your team will ask (and how I’d answer)

“Can we just use an AI detector?”

You can, but don’t treat it like a verdict. Detectors are inputs, not judges. They drift as models change, and they’re vulnerable to paraphrasing.

A better use: detectors as a routing mechanism—flag for review, adjust distribution, request provenance.

“What if the writing is AI-assisted but factual?”

Then the problem isn’t the tool—it’s accountability. AI-assisted can be perfectly fine when claims are sourced and reviewable. Wikipedia’s norms point to the right standard: verifiability beats authorship purity.

“How do we avoid false positives on non-native writers?”

Great teams separate language fluency from content integrity.

• Don’t flag people for “awkward English.”
• Flag content for missing evidence, mismatched sources, and unverifiable claims.

That’s fairer and far more accurate.

Where this fits in the AI in Defense & National Security narrative

This series often focuses on surveillance, intelligence analysis, cybersecurity, and mission planning. AI writing detection belongs here because modern security work is increasingly information security—protecting the integrity of what decision-makers read, share, and act on.

Wikipedia’s “Signs of AI writing” page is a reminder that the best defenses aren’t mystical. They’re operational:

• require checkable claims,
• track provenance,
• make verification cheaper than speculation,
• and treat distribution as a trust decision.

If your media or entertainment organization wants better lead quality (subscribers who stick, advertisers who trust, partners who renew), start where Wikipedia starts: build systems that reward verifiable writing and demote ambiguity at scale.

So here’s the forward-looking question I’d put on the table for 2026 planning: When AI can write infinite content, will your brand compete on volume—or on proof?