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


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

  1. 1. #pubcon A Technical Look at Content Presented by: Patrick Stox @patrickstox
  2. 2. #pubcon 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
  3. 3. #pubcon It’s All Been Done Before Right?
  4. 4. #pubcon Query Intent What’s the query trying to address?
  5. 5. #pubcon We’ve All Seen This • Informational • Navigational • Transactional
  6. 6. #pubcon 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
  7. 7. #pubcon 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
  8. 8. #pubcon I Need You To Write Quality Content
  9. 9. #pubcon What Is Quality Content?
  10. 10. #pubcon 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
  11. 11. #pubcon 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.
  12. 12. #pubcon 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
  13. 13. #pubcon Bing Has A Nice Model https://blogs.bing.com/search-quality-insights/2014/12/08/
  14. 14. #pubcon 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…)
  15. 15. #pubcon Google Has More In Webmaster Academy • Useful and informative • More valuable and useful than other sites • Credible • High-quality • Engaging
  16. 16. #pubcon There’s More! • Readability • Spelling • Grammar • Broken Links • Facts or Incorrect Information
  17. 17. #pubcon 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
  18. 18. #pubcon 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
  19. 19. #pubcon 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
  20. 20. #pubcon Sprinkle Some Keywords
  21. 21. #pubcon Use Any Of The Following As Guides
  22. 22. #pubcon 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.
  23. 23. #pubcon 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.
  24. 24. #pubcon 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.
  25. 25. #pubcon BM25 Like TF-IDF but takes into account document length. Used by Common Search (building a nonprofit search engine) https://about.commonsearch.org/
  26. 26. #pubcon N-grams Unigram, bigram, trigram, four-gram, five-gram. Basically co-occurring words and phrases.
  27. 27. #pubcon Word2Vec Predictive instead of count based. Tries to predict source context-words from the target words. One word predicts a nearby word.
  28. 28. #pubcon 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.
  29. 29. #pubcon Word2Vec
  30. 30. #pubcon Word2Vec
  31. 31. #pubcon Word2Vec Vector Space
  32. 32. #pubcon
  33. 33. #pubcon RankBrain = Word2Vec Probably
  34. 34. #pubcon 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.
  35. 35. #pubcon What Else Can We Look At?
  36. 36. #pubcon Concepts And Entities Used for understanding and context.
  37. 37. #pubcon Autosuggested Phrases Shows what other people are searching for around a topic.
  38. 38. #pubcon What Other Terms Top Pages Rank For Shows what it says.
  39. 39. #pubcon What Questions Are People Asking?
  40. 40. #pubcon Remember That These Are All Guides, Not Absolutes!
  41. 41. #pubcon Thank You! Patrick Stox @patrickstox