宣传文案审校与润色

AI Content Detection Patterns#

Reference for ai_content_detector.py. Explains the three detection methods and how to humanize flagged content.

Method 1: Burstiness (sentence length variance)#

What it measures: The coefficient of variation (CV) of sentence lengths across the text.

Why it works: Human writers naturally vary between short punchy sentences (4-8 words) and longer explanatory ones (20-35 words). AI tends to produce consistently medium-length sentences (12-20 words), creating a "flat" rhythm.

CV Range	Interpretation	AI Probability
0.50+	High variance — natural human rhythm	0-30%
0.35-0.49	Moderate variance — could be either	30-50%
0.20-0.34	Low variance — suspiciously uniform	50-80%
< 0.20	Very flat — strong AI signal	80-100%

How to fix flagged text:

• Deliberately insert short sentences. "This matters." "Here's why."
• Break one long sentence into two short ones, then follow with a 25+ word sentence
• Use fragments where tone allows. "Not always. But often enough."
• Vary paragraph length too: alternate 1-sentence and 3-4 sentence paragraphs

Method 2: Vocabulary diversity (Type-Token Ratio)#

What it measures: The ratio of unique words to total words in sliding 200-word windows.

Why it works: AI models tend to reuse the same "safe" vocabulary — common verbs, generic adjectives, standard connectors. Human writers use more domain-specific terminology, colloquialisms, and varied word choices.

TTR Range	Interpretation	AI Probability
0.60+	Rich vocabulary — likely human	0-25%
0.45-0.59	Average — could be either	25-50%
0.35-0.44	Repetitive — AI-like	50-75%
< 0.35	Very repetitive — strong AI signal	75-100%

How to fix flagged text:

• Replace generic verbs ("use", "make", "get") with specific ones ("wield", "craft", "extract")
• Add domain jargon where your audience expects it
• Vary connectors: instead of always "however", try "still", "yet", "that said", "then again"
• Remove filler phrases that inflate word count without adding meaning

Method 3: Known AI phrases#

30 phrases that appear disproportionately in LLM-generated text. These aren't wrong individually — some are perfectly fine in context — but high density signals AI origin.

Density thresholds:

Density (per 1K words)	Interpretation
0-2	Normal range
3-5	Elevated — review flagged phrases
6+	High — likely AI-generated

The 10 most common AI phrases to watch:

1. "In today's digital landscape" — replace with specific context
2. "It's worth noting that" — just state the fact
3. "Leverage" — use "use" unless specifically about financial leverage
4. "Delve into" — use "explore", "examine", or "look at"
5. "Game-changer" — use a specific description of impact
6. "Comprehensive guide" — be specific about what's covered
7. "Seamlessly integrate" — describe the actual integration
8. "Robust solution" — describe what makes it robust
9. "Cutting-edge" — name the specific advancement
10. "Empower you to" — just say what it enables

The fix: Replace generic AI phrases with specific, concrete language. "In today's digital landscape" → "Since Google's March 2025 core update". "Leverage AI tools" → "Use GPT-4 for first-draft outlines".

Composite scoring#

Composite = Burstiness × 0.35 + Vocabulary × 0.30 + Phrases × 0.35

0-20:  LIKELY_HUMAN  — no action needed
21-50: MIXED         — review flagged passages, humanize selectively
51-100: LIKELY_AI    — significant rewriting recommended

Important caveats#

• This is heuristic, not proof. Technical documentation often has low burstiness and TTR naturally.
• Some AI phrases are perfectly appropriate in context. Don't mechanically remove them all.
• The goal is to make content SOUND human, not to prove it IS human.
• Run this tool AFTER writing, not during — it's an editing pass, not a writing constraint.