The Multilingual Quality Divide: SEO and EEAT Scores Across 30+ Languages
Two-thirds of the web is in English. The other third — spread across 30+ languages and representing billions of users — is largely unmeasured. English makes up 49.2% of web content despite English speakers being only 25.9% of internet users. International SEO specialists talk about hreflang errors (75% of international sites have them) and localization strategies, but nobody has benchmarked actual quality metrics across languages at scale.
We cross-referenced 1.4 million classified URLs from LLMSE's database against SEO, E-E-A-T, WCAG accessibility, readability, and sentiment grades — broken down by detected language. The results challenge assumptions: Dutch sites produce better content quality than English ones. Vietnamese sites have the best SEO. And readability scores are meaningless across language boundaries.
The Language Map: 1.4 Million URLs
LLMSE's classification engine auto-detects content language for every URL it processes. Here's the distribution across the 17 languages with 4,500+ URLs — large enough for statistical analysis:
| Rank | Language | URLs | Share |
|---|---|---|---|
| 1 | English | 949,639 | 67.4% |
| 2 | German | 56,749 | 4.0% |
| 3 | French | 40,421 | 2.9% |
| 4 | Chinese | 38,298 | 2.7% |
| 5 | Spanish | 32,561 | 2.3% |
| 6 | Japanese | 28,363 | 2.0% |
| 7 | Dutch | 25,568 | 1.8% |
| 8 | Portuguese | 15,116 | 1.1% |
| 9 | Vietnamese | 13,977 | 1.0% |
| 10 | Italian | 11,884 | 0.8% |
| 11 | Indonesian | 11,093 | 0.8% |
| 12 | Polish | 10,160 | 0.7% |
| 13 | Turkish | 9,462 | 0.7% |
| 14 | Korean | 7,440 | 0.5% |
| 15 | Czech | 6,095 | 0.4% |
| 16 | Danish | 6,044 | 0.4% |
| 17 | Swedish | 4,855 | 0.3% |
English dominates but doesn't monopolize. At 67.4%, English is the clear majority — yet nearly a third of the classified web operates in other languages. German, French, Chinese, and Spanish each exceed 30,000 URLs. Even the 17th-ranked language (Swedish) has nearly 5,000 data points.
The full dataset includes 140+ detected languages, from Yoruba (3,724 URLs) and Ukrainian (4,519) to Tagalog (4,224) and Thai (2,858). We focus analysis on the 17 languages above where sample sizes support reliable comparisons.
SEO Quality by Language
SEO grades measure technical optimization: meta tags, heading structure, image alt text, mobile responsiveness, and page speed indicators. Here's how each language segment performs:
| Language | URLs w/ SEO | A+B Rate | A+B+C Rate | F Rate |
|---|---|---|---|---|
| Vietnamese | 5,629 | 5.1% | 14.1% | 77.0% |
| Indonesian | 3,545 | 2.0% | 7.7% | 78.6% |
| Turkish | 3,213 | 5.4% | 11.6% | 82.6% |
| Swedish | 2,548 | 2.1% | 5.5% | 87.9% |
| Dutch | 12,792 | 1.0% | 3.4% | 91.6% |
| French | 20,044 | 0.8% | 3.1% | 92.0% |
| German | 32,212 | 0.8% | 3.3% | 92.1% |
| Czech | 3,551 | 0.5% | 4.1% | 92.6% |
| Italian | 4,223 | 0.7% | 2.7% | 93.2% |
| Polish | 5,478 | 0.8% | 2.6% | 93.2% |
| Portuguese | 8,481 | 0.6% | 2.2% | 93.3% |
| English | 570,800 | 0.4% | 1.7% | 94.2% |
| Spanish | 14,954 | 0.5% | 2.1% | 94.2% |
| Korean | 4,025 | 0.7% | 2.4% | 94.7% |
| Japanese | 15,636 | 0.2% | 1.1% | 95.7% |
| Danish | 4,354 | 0.3% | 1.2% | 96.5% |
| Chinese | 32,292 | 0.1% | 0.7% | 96.9% |
The standout finding: Vietnamese sites have the highest SEO pass rate at 5.1% A+B, with only 77% scoring F — compared to English at 94.2% F. Turkish (5.4% A+B) and Indonesian (2.0% A+B) also significantly outperform English.
This likely reflects composition bias: Vietnamese, Turkish, and Indonesian web presences in our dataset skew toward commercial and business sites that invest in SEO. The English web, by contrast, includes millions of personal pages, parked domains, and unmaintained sites that drag the average down.
Chinese and Japanese sites score worst on SEO — 96.9% and 95.7% F rates respectively. CJK languages face unique technical SEO challenges: character encoding issues, difficulty with URL structures, and less mature SEO tooling ecosystems compared to Western languages.
E-E-A-T Content Quality by Language
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) grades measure content quality signals — author credentials, source citations, privacy policies, and organizational transparency. This is where the data gets interesting:
| Language | URLs w/ EEAT | A+B Rate | D+F Rate |
|---|---|---|---|
| Dutch | 8,836 | 36.6% | 39.5% |
| English | 435,625 | 29.7% | 50.4% |
| Korean | 2,881 | 27.4% | 40.5% |
| Italian | 2,082 | 24.8% | 51.9% |
| Czech | 2,760 | 19.8% | 39.0% |
| Spanish | 10,806 | 18.8% | 54.7% |
| Portuguese | 6,349 | 15.8% | 45.0% |
| French | 13,752 | 14.1% | 56.2% |
| Indonesian | 807 | 14.6% | 46.8% |
| Polish | 4,028 | 14.1% | 48.3% |
| Japanese | 12,061 | 12.8% | 57.3% |
| Vietnamese | 3,394 | 10.8% | 65.4% |
| Turkish | 1,757 | 10.4% | 52.9% |
| German | 24,429 | 9.8% | 58.3% |
| Danish | 3,753 | 8.4% | 71.2% |
| Chinese | 29,711 | 8.0% | 60.0% |
| Swedish | 1,702 | 6.7% | 67.6% |
Dutch outperforms English on content quality. At 36.6% A+B pass rate versus English's 29.7%, Dutch-language sites produce measurably stronger E-E-A-T signals. This aligns with the Netherlands' position as a digitally mature market with high web literacy — the country ranks consistently in the top 5 of the EU Digital Economy and Society Index.
Korean (27.4%) and Italian (24.8%) also beat English. Korean sites benefit from strong corporate web presence (Samsung, LG, Naver ecosystem), while Italian sites show strength in author attribution and organizational schema.
German underperforms at 9.8% — a surprising result for Europe's largest economy. The 58.3% D+F rate suggests German sites focus on functionality over content quality signals. German data privacy culture (strong GDPR enforcement) may paradoxically reduce trust signals like contact information and author bios.
Vietnamese scores 10.8% despite leading on SEO — demonstrating that technical optimization and content quality are independent dimensions. A site can have perfect meta tags but no author credentials.
WCAG Accessibility by Language
WCAG 2.1 Level A accessibility checks — alt text, form labels, heading hierarchy, landmark regions, and more — reveal which language communities build the most accessible web:
| Language | URLs w/ WCAG | A+B Rate | F Rate |
|---|---|---|---|
| Japanese | 1,104 | 42.0% | 19.7% |
| English | 24,990 | 32.6% | 26.8% |
| German | 7,016 | 32.5% | 24.3% |
| French | 2,643 | 32.0% | 28.0% |
| Swedish | 481 | 31.0% | 39.7% |
| Dutch | 2,497 | 30.8% | 30.5% |
| Czech | 1,222 | 30.0% | 37.4% |
| Korean | 197 | 28.9% | 47.7% |
| Portuguese | 2,178 | 28.7% | 36.9% |
| Polish | 1,670 | 28.6% | 31.6% |
| Danish | 505 | 27.1% | 26.5% |
| Spanish | 2,444 | 25.6% | 37.0% |
| Chinese | 544 | 23.7% | 41.4% |
| Indonesian | 168 | 22.0% | 47.0% |
| Italian | 262 | 19.5% | 33.2% |
| Turkish | 238 | 16.0% | 55.9% |
| Vietnamese | 282 | 9.6% | 43.3% |
Japanese sites lead accessibility at 42.0% — nearly 10 percentage points ahead of English. Japan's JIS X 8341-3 accessibility standard (harmonized with WCAG) has been mandatory for government websites since 2016, and major Japanese corporations have adopted it broadly. The cultural emphasis on universal design translates directly into web implementation. For context, the WebAIM Million 2025 report found that Korean-language pages average 86 accessibility errors per page versus ~40 for English — our WCAG grading data aligns with these findings.
Western European languages cluster together between 30-33%, reflecting the influence of the EU Web Accessibility Directive (2016) and the European Accessibility Act (effective June 2025, with penalties up to 100,000 EUR or 4% of annual revenue).
Vietnamese sites rank last at 9.6% — another contrast with their SEO leadership. This reinforces that different quality dimensions are genuinely independent: a technically optimized site can still be inaccessible.
The Readability Trap: Why Cross-Language Scores Are Misleading
LLMSE's readability analyzer uses the Flesch Reading Ease formula — a well-established metric for English that produces grades from A (easy) to F (very difficult). The results across languages are striking, but for the wrong reasons:
| Language | A+B Rate | F Rate |
|---|---|---|
| Korean | 67.1% | 23.9% |
| Czech | 64.8% | 7.2% |
| Japanese | 64.6% | 22.7% |
| Danish | 62.2% | 12.9% |
| Vietnamese | 57.3% | 32.6% |
| Dutch | 56.3% | 12.1% |
| Chinese | 52.3% | 32.0% |
| Swedish | 51.2% | 12.8% |
| French | 36.7% | 13.9% |
| Turkish | 36.1% | 16.4% |
| English | 35.7% | 23.1% |
| German | 29.5% | 17.1% |
| Polish | 17.6% | 14.9% |
| Portuguese | 11.2% | 23.2% |
| Indonesian | 9.9% | 33.5% |
| Spanish | 6.6% | 27.5% |
| Italian | 5.0% | 38.9% |
Korean, Czech, and Japanese "score" over 64% A+B? Italian and Spanish score under 7%? These numbers are artifacts, not insights.
The Flesch Reading Ease formula measures sentence length and syllable count — both of which vary systematically by language family, not by writing quality. Romance languages (Spanish, Italian, Portuguese) use longer words with more syllables than Germanic or CJK languages. A perfectly clear Spanish sentence will always "score" harder than a comparable English one.
This is a known limitation. Researchers have developed adapted formulas for specific languages — the Flesch-Amstad for German, the Gulpease index for Italian, the Fernández Huerta formula for Spanish — but no universal cross-language readability metric exists. As noted in research on multilingual readability, "the notion that a greater number of syllables means greater difficulty, although perhaps fine with English, becomes problematic when we apply it to other languages."
The takeaway: readability scores are only valid within a single language. Our English readability grades remain meaningful. But comparing English readability to Spanish readability is comparing apples to syllable structures.
Sentiment Across Languages
Sentiment analysis classifies content as Good, Neutral, or Bad based on emotional content. The overwhelming majority of web content is positive across all languages — but the distribution of negative content varies substantially:
| Language | Good | Neutral | Bad | Bad Rate |
|---|---|---|---|---|
| Indonesian | 85.4% | 14.4% | 0.07% | Lowest |
| English | 84.2% | 15.7% | 0.11% | — |
| Dutch | 86.3% | 13.5% | 0.16% | — |
| German | 88.3% | 11.5% | 0.18% | — |
| Italian | 80.8% | 19.0% | 0.19% | — |
| Portuguese | 84.1% | 15.6% | 0.29% | — |
| Spanish | 85.9% | 13.8% | 0.37% | — |
| French | 85.2% | 14.4% | 0.39% | — |
| Swedish | 81.0% | 18.6% | 0.43% | — |
| Vietnamese | 76.4% | 23.1% | 0.50% | — |
| Danish | 82.4% | 17.1% | 0.53% | — |
| Polish | 83.2% | 16.2% | 0.63% | — |
| Korean | 81.5% | 17.7% | 0.82% | — |
| Turkish | 75.3% | 23.8% | 0.89% | — |
| Japanese | 85.6% | 13.3% | 1.17% | — |
| Chinese | 75.4% | 23.1% | 1.50% | — |
| Czech | 83.6% | 12.0% | 4.38% | Highest |
Czech stands out at 4.38% negative content — 40 times the rate of Indonesian. This is partly explained by the Czech Republic's active cybersecurity and infosec community, which produces content about threats and vulnerabilities classified as negative sentiment. It may also reflect a cultural tendency toward direct, critical writing in Czech web publishing.
Chinese content has the second-highest negative rate at 1.50% — 14 times the English rate. Chinese web content in our dataset includes a higher proportion of news aggregators and discussion forums where negative topics surface more frequently.
Indonesian is the most positive at 0.07% — possibly reflecting content composition skewed toward e-commerce and tourism, which tend toward promotional (positive) language.
The Quality Matrix: Which Languages Lead Overall?
Combining SEO, E-E-A-T, and WCAG pass rates (excluding readability due to cross-language invalidity), we can construct a composite quality picture:
| Language | SEO A+B | EEAT A+B | WCAG A+B | Composite |
|---|---|---|---|---|
| Dutch | 1.0% | 36.6% | 30.8% | Strong EEAT |
| English | 0.4% | 29.7% | 32.6% | Balanced |
| Korean | 0.7% | 27.4% | 28.9% | Strong EEAT |
| Japanese | 0.2% | 12.8% | 42.0% | Strong WCAG |
| Vietnamese | 5.1% | 10.8% | 9.6% | Strong SEO |
| Turkish | 5.4% | 10.4% | 16.0% | Strong SEO |
| Czech | 0.5% | 19.8% | 30.0% | Balanced |
| French | 0.8% | 14.1% | 32.0% | Strong WCAG |
| German | 0.8% | 9.8% | 32.5% | Strong WCAG |
| Spanish | 0.5% | 18.8% | 25.6% | Balanced |
| Chinese | 0.1% | 8.0% | 23.7% | Lagging |
No language leads on all three dimensions. Each language community has developed different strengths:
- Dutch excels at content quality (EEAT) — author attribution, organizational transparency, trust signals
- Japanese leads accessibility (WCAG) — driven by national standards and corporate compliance culture
- Vietnamese and Turkish lead SEO — commercial sites that invest in technical optimization
- English is consistently middle-tier — its massive scale includes both the web's best and worst content
Chinese scores lowest across all three metrics — 0.1% SEO A+B, 8.0% EEAT A+B, 23.7% WCAG A+B. The Chinese web faces compounding challenges: different search engine optimization patterns (Baidu vs. Google), lower adoption of Western EEAT signals, and accessibility standards that are less enforced than in Japan or the EU.
Why This Matters
For International SEO Specialists
The data confirms what practitioners suspected: quality benchmarks vary dramatically by market. An "average" German site has different quality characteristics than an "average" Vietnamese one. International SEO strategies should benchmark against language-specific baselines, not global averages.
For Content Strategists
English content quality is not the gold standard it's assumed to be. Dutch and Korean content communities produce stronger trust and authority signals. Organizations expanding into these markets should study local content patterns rather than simply translating English templates.
For Accessibility Professionals
The EU Web Accessibility Directive and European Accessibility Act (effective June 2025) are already showing impact: Western European languages cluster around 30-33% WCAG pass rates. Japan's JIS standards produce even stronger results at 42%. Markets without accessibility mandates (Vietnam, Turkey, Indonesia) lag significantly — a gap likely to narrow as the European Accessibility Act takes effect and similar regulations spread globally.
For AI Systems
AI answer engines like ChatGPT, Perplexity, and Claude increasingly serve multilingual queries. A Weglot study of 1.3 million citations found that translated sites see 327% more visibility in Google AI Overviews and ChatGPT. The quality gap in E-E-A-T signals across languages means AI systems may have less reliable quality indicators for non-English content — potentially amplifying quality disparities when selecting sources to cite.
For Global Commerce
CSA Research's "Can't Read, Won't Buy" study surveyed 8,709 consumers across 29 countries and found that 76% prefer to buy in their native language — and 40% will never buy from other-language sites. The quality gaps we've measured have real commercial consequences: organizations serving non-English markets face both lower baseline quality in local content ecosystems and users who demand native-language experiences.
Key Takeaways
1. English Volume Doesn't Mean English Quality
English represents 67% of classified URLs but finishes mid-pack on every quality metric. Dutch, Korean, and Czech sites produce stronger content quality signals. Volume and quality are independent dimensions.
2. SEO, EEAT, and WCAG Measure Different Things
Vietnamese sites lead on SEO but trail on accessibility. Japanese sites lead on accessibility but trail on SEO. Dutch sites lead on content quality but are average on SEO. These three quality dimensions are genuinely independent — optimizing one does not improve the others.
3. Readability Formulas Don't Work Across Languages
The Flesch Reading Ease formula produces systematically inflated scores for CJK and Germanic languages and deflated scores for Romance languages. Cross-language readability comparison requires language-specific formulas that don't yet exist at scale.
4. Regulation Drives Accessibility
The correlation between accessibility regulation and WCAG scores is clear: Japan (JIS standard, 42.0%), EU countries (Web Accessibility Directive, ~30-33%), and unregulated markets (Vietnam 9.6%, Turkey 16.0%) form distinct tiers. Policy works.
5. The Chinese Web Faces Compounding Challenges
Chinese-language sites score lowest on SEO (0.1% A+B), near-lowest on EEAT (8.0%), and carry the second-highest negative sentiment rate (1.50%). For organizations operating in the Chinese market, quality improvement opportunities are substantial across every dimension.
Methodology
This report analyzed language, SEO, E-E-A-T, WCAG, readability, and sentiment data for URLs in LLMSE's classification database as of February 25, 2026. Language was auto-detected during classification. Cross-referencing was performed using Redis sorted set intersections between language indices and grade indices.
Analysis was limited to 17 languages with 4,500+ URLs for statistical reliability. Coverage varies by metric — not all URLs have been scored on all dimensions. SEO grades cover the most URLs (794,901 total), while WCAG grades cover fewer (52,578 total). Pass rates are calculated as a percentage of URLs scored in each language, not total URLs in that language.
Limitations: Language detection may misclassify mixed-language content. Flesch Reading Ease is calibrated for English and produces unreliable results for other languages (discussed in detail above). SEO and EEAT grading algorithms were designed with English-language web conventions in mind, which may disadvantage languages with different structural conventions. Sentiment analysis may have varying accuracy across languages.
Check Your Score
Run your site through LLMSE's classification engine to see your SEO, EEAT, WCAG, readability, and sentiment grades. The full audit tool analyzes all dimensions in a single scan — regardless of what language your content is in.
This analysis was conducted using LLMSE, which has classified over 1.4 million websites across SEO, EEAT, WCAG accessibility, readability, and GARM brand safety dimensions. All data reflects the database as of February 2026. To analyze your own site, visit llmse.ai/classify.