marți, 14 iunie 2011

Damn Cool Pics

Damn Cool Pics

Lioness tries to eat human baby at the zoo

Posted: 14 Jun 2011 04:40 PM PDT

Trent the baby nearly ended up as a mid-afternoon snack for Angie, a 400 pound lioness at the Cheyenne Mountain Zoo. Luckily, a thick wall of glass protected little Trent from Angie's jaws and also gave Trent's dad a prime view of the whole adorably scary situation which will no doubt get a mention in this year's Christmas card.

War On The Table

Posted: 14 Jun 2011 04:06 PM PDT

All the models presented at the exhibition "War on the Table" at the Museum of Fine Arts located in Yekaterinburg are handmade. These great models are of German and Soviet WWII vehicles. A lot of small details were meticulously collected together by their authors to impress the visitors. Both adults and children will definitely get pleasure from seeing these small works of art.
















































































Source: wtgm.livejournal

Dogs and Sprinklers

Posted: 14 Jun 2011 01:32 PM PDT

On a hot summer day, there is nothing that a dog likes better than a lawn sprinkler. A dog can have more fun with a lawn sprinkler than anything else. It will provide them with a lot of exercise and also keep them cool. Looks like they are having a blast!





































































Dog trying to drink from a sprinkler in a park. enjoy.

Famous People Without Their Signature Facial Hair

Posted: 14 Jun 2011 12:45 PM PDT

Ever wonder what ol' Honest Abe looked like without his epic neck beard? Well, stop wondering and check out this BuzzFeed post, which compiles pictures of 15 famous folks both with and without their facial hair. Here are just a few:

Tom Selleck


Abe Lincoln


Fidel Castro


Louis CK


Burt Reynolds


Dr. Phil


Zach Galifianakis


George Lucas


Nick Offerman


ZZ Top


Pat Morita (Mr. Miyagi)


Che Guevara


Luciano Pavarotti


Chuck Norris


Weird Al

Huge Tuna

Posted: 14 Jun 2011 12:32 PM PDT

For 35 year-old local fisherman Eli Saban of Ashdod fishing day ended in a state of shock. Amateur fisherman caught a rare tuna weighing about 150 kg. According to experts, from the Japanese fish market in Tokyo, the fish can be sold for 125,000 dollars. Such a high price due to the fact that this kind of tuna is used only for expensive sushi. So If you caught this tuna and sold it you would probably be able to buy a supercar. So Don't miss them.

Taiwan Weather Girls

Posted: 13 Jun 2011 09:13 PM PDT

Are you having a good weekend? Celebrate this lazy Sunday with Kitty. Maybe a movie later. Meow!

25 Ugly and Disturbing Animals

Posted: 13 Jun 2011 09:00 PM PDT

Who knew there were so many unattractive animals in the world? I would not recommend taking very many of these animals as pets, or at least not showing them off to too many people.


















































Source: ktla

Celebrities at Gas Stations

Posted: 13 Jun 2011 07:20 PM PDT

Yeah, they do that too… themselves.

Kim Kardashian


Jessica Alba


AnnaLynne McCord


John Mayer


Mischa Barton


Ryan Phillippe


Janice Dickinson


Katie Price


Ellen Pompeo


Scott Disick


Leona Louise


Audrina Patridge


Christian Slater


Breckin Meyer


Vanessa Hudgens


Balthazar Getty


Heather Morris


Phoebe Price

The Walton Sextuplets: Then and Now

Posted: 13 Jun 2011 05:50 PM PDT

The Walton sextuplets were born in Liverpool, England on 18 November 1983 and were the world's first all-female surviving sextuplets, and the world's fourth known set of surviving sextuplets. The children are Hannah, Luci, Ruth, Sarah, Kate and Jenny.

The children were born to Graham and Janet (born 1952) Walton who had been trying to have children for several years and had just been accepted as potential adoptive parents before their thirteenth attempt at fertility treatment bore spectacular results. The children grew up in the family's seven-bedroom home in Wallasey, Merseyside.



But now, after spending much of the past decade out of the limelight to live more privately, the 27-year-olds are all keen to stress their individuality. Two of the sisters, who were born within four minutes of each other on November 18, 1983, are now brunettes. And while the other four still have their blonde locks, there are three different shades between them.

The siblings have also struck out in different directions for their careers, with some working abroad and others taking jobs in the UK. Yet four still live in the eight-bedroom family home in Wallasey, Wirral, with parents Graham and Janet, a fifth will be returning this summer and the sixth lives just down the road.
















Source: dailymail
Publicat de 1 la 19:09 0 comentarii

SEOmoz Daily SEO Blog

SEOmoz Daily SEO Blog

Does Google Use Facebook Shares to Influence Search Rankings?

Posted: 13 Jun 2011 02:37 PM PDT

Posted by Matt Peters

Intro from Rand - this post comes from Dr. Matt Peters, SEOmoz's data scientist. He came on board in February of this year after stints at Harvard (working on climate science models), Washington Mutual, JP Morgan and Fannie Mae (analyzing mortgage securities) and more (including some research into Google Places rankings last November). Matt's particularly passionate about bringing the best practices of scientific and quantitative analysis to the world of inbound marketing, and I'm very excited to welcome him to the Moz community.

One of the most interesting findings from our 2011 Ranking Factors analysis was the high correlation between Facebook shares and Google US search position. In fact, Facebook shares was the highest correlated single factor among the 100+ factors we examined:

Facebook and other social media correlations with Google search position

This blog post presents some modeling results that look at whether Google may be using Facebook shares directly in it's relevance calculation, or whether the correlation between Facebook shares and search position is coincidental, aka the byproduct of other causal factors.

Correlation and Causation

As we have said time and time again on this blog, in our presentations and when speaking, correlation is not causation. However, this post will discuss issues of both correlation and causation, so for the purposes of this discussion it's important to understand the relationship between them on a deeper level. Correlation does not, in general, imply causation. However, two things that are causally related will often be well correlated. Correlation can therefore only be used to support or deny causation, but not to show it directly. Put another way, if we have some prior, a priori, reason to believe that two things are related, correlation can be used as a tool (with rigorous statistical underpinnings) to support the relationship. In turn, weak correlation can be used to weaken the argument that two things are related.

Before we started our work on the 2011 Ranking Factors, we had some reasons to believe that Facebook data may be used by Google. There was an interview with Google/Bing in December 2010 where they disclosed that they were using social media signals in to rank search results. We also began seeing Facebook share information in our search results, so we knew that Google had access to at least some Facebook data.

Facebook share data appears in Google web search results

Even having this public comment from Google and seeing the Facebook data in search results (you can also observe them in Google realtime, e.g. here), we were still surprised at the size of the correlation in our ranking factor result and we wondered whether it was causal or the result of other factors like links. As a simple check, we ran some hasty partial correlations to control for links and concluded that they accounted for some of the correlation, but not all. This appeared to be another data point to support causation, or so we thought...

SMX Advanced

At SMX Advanced in Seattle last week, Rand Fishkin presented the main results from the ranking factors work, including the partial correlations controlling for links. We were still not confident that Facebook shares were being used directly, so Rand was very careful to add several caveats saying that these might not be causally related. You can see his presentation embedded below: 

Interesting Data from the 2011 Ranking Factors
View more presentations from Rand Fishkin

That evening, Matt Cutts, the head of Google's web spam team said that they do not crawl Facebook "wall" pages and implied that they don't use Facebook shares for ranking. His language was somewhat vague (leaving room open as to whether some forms of Facebook data are used or come via a special feed), however we and many others felt that Matt had implied that Facebook shares, specifically, are not part of their web ranking algorithm. Rand pointed out that Google does have some access to Facebook data overall and set up a small-scale test to determine if Google would index content that was solely shared on Facebook. To date, that page has not been indexed, despite having quite a few shares (64 according to the OpenGraph).

Both Rand and Matt's talks and the subsequent discussion with Danny Sullivan on Twitter was well covered over at Search Engine Land. 

Sitting in the audience, I began to think about the implications of this new information. If Google wasn't using Facebook shares, then the high correlation must be explainable with things that they are using. I made a short list of different factors that Google might be using to determine relevance that would also be correlated with FB shares:

  • Links. Pages that are heavily shared on Facebook tend to also be heavily linked to.
  • Other social media signals. Pages that are shared on Facebook also tend to be tweeted about and shared through Google Buzz.
  • Quality content. People share pages that they find interesting and have high quality content. This drives positive usage signals (time spent on page, bounce rate, etc) that might be used.
  • Association with well known, quality brands. There is likely more interaction with well known brands then lesser known brands, and this might drive deeper engagement with them on Facebook.

Building a better model for Facebook shares

I thought back to the partial correlations I had run a few months prior. At the time, I was mainly interested in a first look that could be done in a few hours, so I chose partial correlations using a limited set of four metrics from Linkscape as the control variables. Partial correlations use a linear regression model to predict the correlation variables (in this case Facebook shares and search position), the simplest type of regression model. It has the benefit be being well established and easy to use, but falls short when the underlying relationship is more complicated or non-linear. In addition, I didn't try to control for other social media signals since at the time we were interested in links.

I began to wonder if the results would change if I tried a more complicated model using Twitter/Google Buzz and all the available link metrics from Linkscape so I set out to build the model. Before describing the model, it's important to write down our modeling assumptions. They are:

  • Google uses link metrics for ranking, similar to those available in our Linkscape API.
  • Google uses other social media data, in particular Tweets and Google Buzz shares to rank.

We are testing whether Facebook shares provide any increase in predictive power beyond these factors.

To build the model, I took a subset of the full dataset used in the ranking factors report (scrubbed for data quality, but otherwise unchanged). The baseline mean Spearman correlation between search position and Facebook shares in this data is 0.30. Then I took all 61 keyword agnostic link metrics used in the ranking factors and (1) ran them through a generic filter to transform them to something close to Gaussian and (2) did a principal component analysis and kept the first 19 principal components that explain 99% of the variability in the original data. This allows me to use a complicated non-linear model without worrying about collinearity issues. I augmented the 19 principal components with three social media metrics, namely the number of Tweets and Topsy Influential metrics from Topsy and the number of Google Buzz shares.

I used a two step process to fit the actual model. First, about 33% of the URLs don't have any Facebook shares and the rest have at least one share. Fitting a regression model to a distribution with a big spike at 0 is generally not a good idea, so I first fit a binary classifier to the cases for 0 / 1 or more shares. Then, I fit a non-linear regression model to the remaining data with at least one share. Over fitting was controlled through cross-validation. The total predicted number of shares can be computed from the output of these two pieces.

The final model performs moderately well, with the correlation coefficient between actual and predicted shares at 0.73. However, the mean Spearman correlation between the predicted number of shares and search position is 0.27, nearly as large as the baseline 0.30. This strongly suggests that our interpretation of Matt Cutts' statement is correct and Google is not using Facebook share data directly to rank.

Takeaways

  • Facebook shares, at least as related to Google searches, act as a sort of "super-metric", encompassing a variety of different factors (similar to SEOmoz's Page Authority and Domain Authority).
  • Don't stop sharing and generating brand engagement through Facebook!  Driving deeper engagement through social media can only help your brand and likely has other positive benefits (by generating tweets or links, for example).
  • Earning Facebook shares (probably) will not directly boost your Google rankings (though it may have positive effects that indirectly promote links, tweets and other signals Google may use).
  • The process of doing this type of correlation work and sharing the results openly started a great dialogue in the search community, and through that we all learned more about how search works. We plan to do more work like this, and are planning a project for the Fall to compare Google and Bing results.

Finally, I'd like to thank Danny Sullivan and Matt Cutts for sparking this work through their discussion at SMX Advanced.


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