2nd/10_Wiki/Topic_Programming/AI_and_ML/Superficiality-Metrics.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-superficiality-metrics	Superficiality Metrics	10_Wiki/Topics	verified	self	Engagement Quality Metrics Depth Metrics Content Quality Signals	none	B	0.85	applied	metrics content-quality engagement evaluation		2026-05-10	pending	language framework python pandas

Superficiality Metrics

매 한 줄

"매 engagement 의 depth 측정". CTR / time-on-page 같은 surface metric 만 보면 clickbait 의 reward → 매 deeper signal (scroll completion, return visit, comment quality, downstream conversion) 의 measure 의 필요. 2026 의 LLM-as-judge 의 quality scoring 의 mainstream.

매 핵심

매 surface vs depth metric

• Surface: CTR, time-on-page, bounce rate, like count.
• Depth: scroll depth, dwell quality (focus events), return visit %, share-with-comment, subscription, downstream action (purchase, signup).

매 LLM-judged content quality

• Coherence: 매 logical flow.
• Substantive density: 매 facts / claim 단위 의 information.
• Originality: 매 generic LLM-output 의 detection.
• Actionability: 매 reader 가 take-away 의 가능성.

매 응용

1. Content recommendation ranking (YouTube, TikTok 의 newer signals).
2. Knowledge-base quality gating (Wiki article 의 acceptance).
3. Education platform 의 learning outcome 측정.
4. Newsletter / blog 의 ROI evaluation.

💻 패턴

Scroll depth tracking

let maxScroll = 0;
window.addEventListener('scroll', () => {
  const scrollPct = window.scrollY / (document.body.scrollHeight - window.innerHeight);
  if (scrollPct > maxScroll) maxScroll = scrollPct;
}, { passive: true });

window.addEventListener('beforeunload', () => {
  navigator.sendBeacon('/analytics', JSON.stringify({
    page: location.pathname,
    maxScroll,
    duration: performance.now()
  }));
});

Dwell quality (focus + scroll)

let focusedTime = 0;
let lastFocusStart = document.hasFocus() ? performance.now() : null;

document.addEventListener('visibilitychange', () => {
  if (document.hidden && lastFocusStart != null) {
    focusedTime += performance.now() - lastFocusStart;
    lastFocusStart = null;
  } else if (!document.hidden) {
    lastFocusStart = performance.now();
  }
});

LLM-as-judge quality score

from anthropic import Anthropic
client = Anthropic()

def score_content(text: str) -> dict:
    resp = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=512,
        messages=[{
            "role": "user",
            "content": f"""Rate the following article on 4 axes (0-10 each):
- coherence (logical flow)
- density (info per paragraph)
- originality (vs generic LLM output)
- actionability (reader takeaway)

Return strict JSON: {{"coherence": N, "density": N, "originality": N, "actionability": N, "rationale": "..."}}

ARTICLE:
{text[:8000]}"""
        }]
    )
    import json
    return json.loads(resp.content[0].text)

Composite depth score

import numpy as np

def depth_score(metrics: dict) -> float:
    # weights tuned on labeled training set
    w = {
        'scroll_completion': 0.15,
        'focused_dwell_ratio': 0.25,
        'return_within_7d': 0.20,
        'downstream_action': 0.25,
        'share_with_comment': 0.15,
    }
    return sum(w[k] * metrics.get(k, 0) for k in w)

Clickbait detector heuristic

def clickbait_signal(row):
    # high CTR + low depth = clickbait
    if row['ctr'] > 0.10 and row['depth_score'] < 0.3:
        return 1.0
    return 0.0

Pandas pipeline

import pandas as pd
df = pd.read_parquet('events.parquet')
agg = df.groupby('article_id').agg(
    ctr=('clicks', 'sum') / ('impressions', 'sum'),
    avg_scroll=('max_scroll', 'mean'),
    return_rate=('returned_7d', 'mean'),
).assign(depth_score=lambda d: 0.4*d.avg_scroll + 0.6*d.return_rate)

매 결정 기준

상황	Metric
매 ad-supported (need clicks)	CTR + minimal depth floor
매 subscription / paid	depth_score primary
매 education / learning	actionability + post-test outcome
매 knowledge wiki	LLM coherence + density
매 social platform	share-with-comment, return visit

기본값: 매 composite depth score (50% behavioral + 50% LLM-judged).

🔗 Graph

• 부모: Evaluation
• Adjacent: LLM-as-Judge · Goodhart_s-Law

🤖 LLM 활용

언제: 매 content recommendation 의 reranking signal, KB article quality gate, AB test 의 secondary metric. 언제 X: 매 small sample (variance 너무 큼), 매 acquisition-stage funnel (CTR primary).

❌ 안티패턴

• Single metric optimization: Goodhart — 매 CTR alone optimize 하면 clickbait.
• LLM judge 의 prompt drift: 매 pinned model + temperature 0 + version log 의 필수.
• Depth metric 의 latency: return-visit 7d → 매 delayed feedback. 매 surrogate (focused dwell) 도 함께.

🧪 검증 / 중복

• Verified (Goodhart 1975; Zheng et al. 2023 LLM-as-judge; YouTube Watch Time → "Valued Watch Time" pivot ~2017).
• 신뢰도 B (매 weighting 의 domain-dependent).

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — surface vs depth + LLM judge + composite scoring