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A Technical Look at Content

Presented by:Patrick Stox@patrickstox

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Normal On-Page SEO• Title tag• Meta Description• Canonical• Header Tags• Image name and alt attributes• Keyword in URL• Speed

• HTTPS• Pagination• HREFLANG• Mobile Friendly• Content visible• Internal links• Indexable

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It’s All Been Done Before Right?

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Query IntentWhat’s the query trying to address?

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We’ve All Seen This• Informational• Navigational• Transactional

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Google’s Quality Raters Guidelines Has

• Know query, some of which are Know Simple queries

• Do query, some of which are Device Action queries• Website query, when the user is looking for a

specific website or webpage• Visit-in-person query, some of which are looking

for a specific business or organization, some of which are looking for a category of businesses

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Website FeaturesWhat would you expect to see when visiting a website?

Physical Store: Address, Phone #, Hours of operationE-Commerce: Pricing, Reviews, Return Policy, Contact

Some niches have things like certification numbers

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I Need You To Write Quality Content

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What Is Quality Content?

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Google Tells You Things Not To Do• Automatically generated content• Participating in link schemes• Creating pages with little or no original

content• Cloaking• Sneaky redirects• Hidden text or links• Doorway pages• Creating pages with malicious behavior,

such as phishing or installing viruses, trojans or other badware

• Scraped content• Participating in affiliate

programs without adding sufficient value

• Loading pages with irrelevant keywords

• Abusing rich snippets markup

• Sending automated queries to Google

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But Google Is Vague On What To Do• Make pages primarily for users, not for search

engines.• Don’t deceive your users.• Avoid tricks intended to improve search engine

rankings. • Think about what makes your website unique,

valuable or engaging. Make your website stand out from others in your field.

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The Good Practices Listed• Monitoring your site for hacking and removing

hacked content as soon as it appears• Preventing and removing user-generated spam

on your site

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Bing Has A Nice Model

https://blogs.bing.com/search-quality-insights/2014/12/08/the-role-of-content-quality-in-bing-ranking

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What Are These?• Topical relevance to the query (“Does it

address the query?”)• Content Quality (as measured by Authority,

Utility, and Presentation), and• Context (“Is the query about a recent topic?”,

“What’s the user’s physical location?” etc…)

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Google Has More In Webmaster Academy

• Useful and informative• More valuable and useful than other sites• Credible• High-quality• Engaging

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There’s More!• Readability• Spelling• Grammar• Broken Links• Facts or Incorrect Information

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How Deep Down The Rabbit Hole Do We Want to Go? -> Readability

• Flesch Kincaid Reading Ease• Flesch Kincaid Grade Level• Gunning Fog Score• Coleman Liau Index• Automated Readability Index (ARI)• SMOG (Simple Measure of Gobbledygook)

• Fog Index• Lix formula• Spache Index• Dale-Chall Index• Dale-Chall Grade

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But Wait, There’s More!• Position of content. Hidden/visible, font size,

styling• Who the author is• What website the content is on• Duplicate/uniqueness, different take, etc.• Semantically related

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Looking At Content Is The Fun Part• Keyword density - times keyword appears on

page / total words on page, expressed as %• LSI (Latent Semantic Indexing) - looks for

closely related words, synonyms, variants

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Sprinkle Some Keywords

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Use Any Of The Following As Guides

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LSALatent Semantic Analysis

Bag of words. Count based models.

It finds words mentioned but not really the meaning. So we might see Hogwarts related to Harry Potter, but not see it as a school for higher learning.

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TF-IDFTerm Frequency – Inverse Document

FrequencyFrequency of a term within a document divided by its frequency in the entire corpus

How important a word is in a document or collection of documents.

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WDF*IDFWithin Document Frequency - Inverse Document Frequency

This is basically keyword density 2.0 with a correction value and weighted across a set of documents.

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BM25Like TF-IDF but takes into account document length.

Used by Common Search (building a nonprofit search engine) https://about.commonsearch.org/

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N-gramsUnigram, bigram, trigram, four-gram, five-gram.

Basically co-occurring words and phrases.

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Word2VecPredictive instead of count based.

Tries to predict source context-words from the target words. One word predicts a nearby word.

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What Can You Do With Word2Vec?• Measure the similarity between words or

documents.• Find most similar words to a word or phrase. • Add and subtract words from each other to find

interesting results.• Visualize the relationship between words in a

document.

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Word2Vec

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Word2Vec

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Word2Vec Vector Space

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RankBrain = Word2VecProbably

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It might be more…Doc2vec correlates labels and words, rather than words with other words.

LDA predicts a word from a global context.

Lda2vec tries to build both word and document topics.

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What Else Can We Look At?

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Concepts And EntitiesUsed for understanding and context.

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Autosuggested PhrasesShows what other people are searching for around a topic.

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What Other Terms Top Pages Rank For

Shows what it says.

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What Questions Are People Asking?

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Remember That These Are All Guides, Not Absolutes!

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Thank You!

Patrick Stox@patrickstox