A Technical Look at Content - PUBCON SFIMA 2017 - Patrick Stox

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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 Intent
What’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 Features
What would you expect to see when visiting a website?
Physical Store: Address, Phone #, Hours of operation
E-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/

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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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LSA
Latent 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-IDF
Term Frequency – Inverse Document
Frequency
Frequency 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*IDF
Within 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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BM25
Like 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-grams
Unigram, bigram, trigram, four-gram, five-gram.
Basically co-occurring words and phrases.

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Word2Vec
Predictive 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 = Word2Vec
Probably

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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 Entities
Used for understanding and context.

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Autosuggested Phrases
Shows 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