Google Panda – Basic Info

First Name: “Content Farm” named by Danny Sullivan. Later Google revealed the name as "Panda".

First Update: February 24th, 2011

Impact: 11.8% of English Queries in US.

Why Panda: Focused on showing users the highest quality, most relevant pages on the web.

What is Content Farm:  Content farms are places with “shallow or low quality content.”

External Pressure - The reason behind Content Farm update: Start-up search engine Blekko blocked sites that were most reported by its users to be spam, which included many sites that fall under the content farm heading. It gained a lot of attention for Google and finally the developed the solution for Content farm for Google search result.

Second is Panda: First one was rolled out on January 28th, 2011, named as “Scraper Update” by Danny Sullivan which affects 2% of queries. The net effect of that update allows searchers are more likely to see the sites that wrote the original content rather than a site that scraped or copied the original site’s content.

Factors - Winners Vs Losers

1. Sites whose pages had fewer and/or less intrusive blocks of advertisements on them tended to be in the winner bucket, while those with more and more intrusive advertising tended to be in the loser group.

2. Likewise, sites whose UI/design would likely be described as more modern, high quality, thoughtful and "attractive" were winners vs. the "ugly" sites that tended to be in the loser bucket.

3. When it came to user-generated-content (UGC) sites, those that tended to attract "thin" contributions (think EzineArticles, Hubpages or Buzzle) lost, while those with richer, often more authentic, non-paid, and not-intended to build SEO value or links (think Etsy, DailyMotion, LinkedIn, Facebook) won.

4. In the "rich content" sector, pages with less usable/readable/easily-consumable content (think AllBusiness, FindArticles) tended to lose out to similarly content-rich sites that had made their work more usable (think LOC.gov, HuffingtonPost)

What signals Google may have used in this Update

• User/usage data - signals like click-through-rate, time-on-site, "success" of the search visit (based on other usage data)

• Quality raters - a machine-learning type algorithm could be applied to sites quality raters liked vs. didn't to build features/factors that would boost the "liked" sites and lower the "disliked" sites. This can be a dangerous way to build algorithms, though, because no human can really say why a site is ranking higher vs. lower or what the factors are - they might be derivatives of very weird datapoints rather than explainable mechanisms.

• Content analysis - topic modeling algorithms, those that calculate/score readability, uniqueness/robustness analysis and perhaps even visual "attractiveness" of content presentation could be used (or other signals that conform well to these).

Sources:
http://www.seomoz.org/blog/googles-farmer-update-analysis-of-winners-vs-losers
http://searchengineland.com/google-forecloses-on-content-farms-with-farmer-algorithm-update-66071