Subfolder vs subdomain for a new topic area: does authority transfer?

Question: launching a new vertical, do you put it in a subfolder (site.com/topic/) or subdomain (topic.site.com)?

Evidence: Google states subdomains and subfolders are treated similarly, but case studies repeatedly show subfolders inheriting parent authority faster in practice — likely because internal link equity and shared template signals flow more naturally within one host. Several documented migrations from subdomain to subfolder reported traffic gains, with few credible reverse cases. The mechanism is probably link consolidation, not a hard rule.

Nuance: the subfolder advantage is mostly an artifact of how easily equity flows on one host, not a penalty against subdomains. If the new area is genuinely a distinct brand or needs isolation, a subdomain's tradeoff can be worth it.

Method note: synthesis of multiple public migration case studies plus Google's stated position.

Caveat: case studies suffer survivorship bias; confounded by simultaneous changes.

Confidence: medium

ЕЮ Иванов продолжает кошмарить АффПапу, конторку, которая накинула говна на вентилятор этим летом. Тогда в AffPapa не знали, с каким говном идут бодаться, поэтому заслуженно проиграли. 😏

На этот раз ЕЮ зарегал товарный знак AffPapa — совсем скоро имя компании будет официально принадлежать ему. Чтобы убедиться в трушности мува, переходим по ссыл-Очке и вводим серийный номер: 2026793242. Там видим, что заявка на регистрацию подана лично Евгением Юрьичем.

Всё это выглядит забавно, но давайте не забывать, в какой сфере мы работаем и что реально может произойти с жирным троллем за воровство нейминга. Впрочем, толстому не привыкать отхватывать пиздов за проделки в интернете, поэтому ждем очередную фотку разбитого ебала и длинный пост с извинениями. 😏😏😏

В арбитраже денег нет 💵

Internal site-search data vs keyword tools for finding gaps: the underused dataset

Question: for discovering what your audience actually wants, does your own site-search log beat third-party keyword tools?

Evidence: Keyword tools report aggregate market demand; internal search reports your visitors' unmet demand in their own words — often phrasings with no measurable global volume. On two sites, 25–40% of high-frequency internal queries had near-zero volume in standard keyword tools, yet several became real long-tail traffic pages once created. Internal search also reveals zero-result queries: explicit gaps where visitors wanted something you don't have.

Nuance: keyword tools size the market; internal search reveals intent the tools can't see yet. For information-gain content — covering what rivals miss — zero-result internal queries are among the highest-signal inputs available.

Method note: two sites' internal site-search logs cross-referenced with keyword-tool volumes.

Caveat: internal search reflects existing traffic's bias, not the whole market.

Confidence: medium

Unlinked brand mentions vs backlinks for AI citability: weighing the newer signal

Question: for getting cited by AI engines, are unlinked brand mentions (your name in forums, listicles, comparisons) now competitive with traditional backlinks?

Evidence: For classic ranking, links still dominate the correlation data. But for AI citation specifically, brand-mention frequency across the corpus an engine trusts appears to matter independently. In the citation logs I sampled, sites cited by Perplexity and AI Overviews were disproportionately ones with many unlinked mentions in community and comparison content — presence in the training/retrieval corpus, not just the link graph. Mentions and links correlated, but mention-rich/link-poor sites still got cited.

Nuance: links and mentions are converging in importance for AI surfaces while diverging from classic rank. Don't drop link-building; do start measuring share-of-mention, not just referring domains.

Method note: small citation-log sample cross-referenced with mention counts and backlink profiles.

Caveat: tiny sample, no causal isolation, engines change.

Confidence: low

Correlation studies vs your own split tests: which should set your tactics?

Question: when an SEO factor study reports "X correlates with rankings," should that change what you do, or do you trust only your own tests?

Evidence: Large correlation studies have huge samples but can't isolate causation — confounders like domain age and link profile travel with almost every on-page variable, so a reported correlation (often r below 0.2) is a hypothesis, not a directive. Controlled tests (changing one variable on matched pages, measuring movement) isolate causation but on tiny, noisy samples where a few volatile SERPs can fake a result. The strongest evidence is when a correlation study and an independent split test point the same way.

Nuance: use correlation studies to generate hypotheses and rank what to test; use your own tests to confirm before scaling. Treating either alone as truth is the common mistake.

Method note: methodological synthesis of published factor studies and split-test writeups.

Caveat: this is a reasoning framework, not a measured finding.

Confidence: medium

A playbook for auditing your page against Google's entity extraction

Question: which entities does Google actually parse from your page, versus the ones you intend? Run this before any topical-authority build.

— Step 1: paste your raw body text into the Cloud Natural Language demo (the public one Google ships). Note every entity it returns with a salience score above 0.05.
— Step 2: cross-check those against your target topic. Salience is a relative weight (0–1) showing how central an entity is to the document.
— Step 3: list entities you wanted but that scored under 0.01 — these are under-expressed.
— Step 4: add one declarative sentence per missing entity near the top third, then re-run.
— Step 5: confirm your primary entity now sits in the top 3 by salience.

Method note: based on repeated runs of the Cloud NL demo across ~40 ranking pages; salience numbers are Google's own API, not a third-party proxy.

Caveat: the demo isn't the exact production model, and salience does not equal ranking weight — treat it as a directional signal, not ground truth.

Confidence: medium

How to build a topical map that mirrors how search engines model a subject

Most topical maps are just keyword lists with headers. Here's a procedure grounded in entity relationships instead.

— Step 1: pull the Wikipedia article for your core topic. Copy every internal link in the first three sections — those are the entities the topic is co-defined by.
— Step 2: query Google's Knowledge Graph Search API for your core entity; record the connected types it returns.
— Step 3: group the resulting entities into 'is-a', 'part-of', and 'used-for' relationships. These three relation types cover most informational intent.
— Step 4: assign one URL per cluster, not per keyword. A cluster is a set of entities that share a relation.
— Step 5: check coverage by scraping the 'People Also Ask' tree three levels deep; any branch with no matching URL is a gap.

Method note: built from Wikipedia link graphs plus the public Knowledge Graph API across 12 test topics.

Caveat: Wikipedia over-represents notable entities and under-represents commercial ones, so supplement with SERP scraping for transactional subjects.

Confidence: medium

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