Here’s the content Google aims to keep out of featured snippets

Here’s the content Google aims to keep out of featured snippets

Recently, Google’s practices around which sites and types of content it may include in featured snippets have raised censorship concerns and talk of blacklists in some circles. We asked Google to clarify its policies around the types of content eligible for featured snippets and how it finds and removes content deemed inappropriate for those placements.

What are featured snippets? Featured snippets are generally blocks of content sourced from pages across the web. Google shows them at the top of search results pages for some search queries. They can show up in paragraph form, with images, they can include bulleted lists, tables and more. They aim to give searchers a concise answer to a query that can be scanned by the user or read aloud Google Assistant.

Over a year ago, Google published a comprehensive guide to featured snippets.

What types of content does Google aim to keep out of featured snippets? Google does not intend to show featured snippets for content that falls within several categories:

  • Sexually explicit content.
  • Hateful content.
  • Violent content.
  • Dangerous and harmful content.
  • Lack consensus on public interest topics content such as categories like civic, medical, scientific and historical issues.

In the context of that last category, Google specifically designed systems to generally prevent Op-Ed content from showing up as featured snippets. That means sites and pages that include highly polarized content that’s unlikely to represent consensus viewpoints on a range of sensitive topics would also be excluded from being shown as featured snippets.

“Featured snippets are a feature within Search that highlights web sources that are likely to contain what you’re looking for. Due to the special formatting they receive, we have policies that prevent us from showing a featured snippet for topics like civics or medical information where the content lacks broad consensus,” a Google spokesperson told Search Engine Land. “Our systems are designed to not show featured snippets that would violate our policies, and we take action if violating snippets still appear. These policies and actions have no impact on how a page ranks in organic search listings.”

How does Google keep content out of featured snippets? Primarily Google designs algorithms to detect  and automatically remove the types of content that don’t conform with its content policies for featured snippets. Google handles way too many searches per day and finds way too much new content per day to rely on humans to manually remove all these types of content. So, Google says, “our systems automatically strive not to show featured snippets that would violate our policies. However, the scale of search is so large that no system can be perfect. This is why we provide a public reporting system.”

Google can generate lists algorithmically to identify a large number of sites likely to contain highly polarized content that would be unlikely to represent consensus viewpoints on a range of sensitive topics, and would thus be likely to violate the policies listed above. Google says the vast majority of sites on this list are not political. Furthermore, Google told us it does not encode any notion of political leaning or preference into Google’s products, including Google Search.

When the algorithmic lists and detection systems fail, Google will take action manually. You can report featured snippets by clicking on the “feedback” link under the featured snippet after a query to notify Google of an issue.

Not penalized in core search. Just because a site is not eligible to show in Google’s featured snippets section does not mean it won’t rank in core web search. Google told us these sites still rank as they normally would in organic search results, there is no impact on ranking and no penalty applied to these sites in normal web rankings.

Why we should care. Featured snippets can be a great source of traffic to a web site from Google search. It can also be the only source of traffic from Google Assistant and Google Home device voice queries. Typically publishers and webmasters want content shown in the featured snippet box for a given query, but if your content falls within these categories, the chances of your content being featured is unlikely.

About The Author

Barry Schwartz is Search Engine Land's News Editor and owns RustyBrick, a NY based web consulting firm. He also runs Search Engine Roundtable, a popular search blog on SEM topics.

Dit artikel is vertaald van Search Engine Land

vaticaanstad bezoeken

Vaticaanstad bezoeken? Ontdek wat er allemaal te zien en doen is in Vaticaanstad, alle bezienswaardigheden Vaticaanstad op een rij.

Stel je voor dat je Vaticaanstad kon zien zoals het bedoeld was. Niet langer volgepropt met massa's mensen, elk knellend past bij elkaar en op zoek naar een kunstwerk waar ze over gehoord hebben, terwijl ze een dun boekje in hun handen houden. Stel je voor dat je een exclusief en - ik durf het te zeggen - geheime kant van Vaticaanstad kunt ervaren. Bijna zoals The Da Vinci Code, maar zonder al te ingewikkelde plot-apparaten en moord.

Het voor de hand liggende vertrekpunt zijn de Vaticaanse Musea. Deze zijn vaak genoemd als de grootste kunstgalerijen ter wereld. Er is zoveel te zien en te vinden binnen de muren van de Vaticaanse Musea dat het onmogelijk zou zijn om alles in één dag te bedekken. De Raphael-kamers, de fresco's van Michelangelo en nog veel, veel meer.

Klik hier om meer te lezen over Vaticaanstad.

SMX Overtime: Here’s how to make SEO gains through data science

SMX Overtime: Here’s how to make SEO gains through data science

I am a senior data scientist at LinkedIn working on SEO and guest experience. I presented at SMX London last month about how to apply data science in SEO. The session covered topics including metrics, A/B testing, SEO vs. SEM cannibalization testing and machine learning for content quality. Here are a few questions from session attendees with my responses.

For A/B testing, do you use any specific tools/processes?

We have an internal infrastructure that supports user-friendly A/B testing setup and does automatic statistical analysis for key metrics. If you are interested, you can see this paper [pdf] about how we do experiments at LinkedIn. If you do not have the internal tools available, you can randomize your target set of URLs and compare your metrics from two groups of URLs using open source statistical test solutions such as R, Python Scipy package, etc.

How do you sample SEO A/B testing? For how long do you run it?

At LinkedIn’s scale, where often we have more than hundreds of thousand URLs in each experiment, we simply randomize URLs into two groups and compare their metric impact. However, when we start the experiment, we roll out the experiment feature gradually from a small percentage to 50% to minimize risk from the experiment.

In terms of the experiment duration, it depends on the type of experiment and the type of product we experiment on. But generally, we try to run it at least a month to give enough time for search engines to crawl the new changes and reflect them in search ranking.

What were the results for SEO vs. SEM cannibalization test?

For the keywords we chose for that specific test, we did not see any impact in organic search traffic. So we were able to move on to roll out the SEM campaign. This learning, however, would not apply to all cases since it would depend on the keywords and pages you target. So I recommend running an experiment before your SEM campaign if cannibalization is your concern.

How did you use content quality for SEO recommendations? Do you force users to add photos or other product recommendations or SEO actions?

Rather than encouraging users to enhance the contents on their profile or other pages, we plan to use this score in SEO use cases such as directory, cross-link, etc. We would like to surface better quality pages to search engines and searchers by using the score.

How do you get into this field (data science and SEO)?

When I was in graduate school with an industrial engineering major, I took a machine learning class and got immediately hooked. The idea of finding patterns and insights from the data fascinated me. In terms of application to SEO, it was only at LinkedIn that I started working in the SEO area and learning about it. Even after working in SEO for three to five years, I think there is still a lot of exciting data science work to be done in this area. It was a great pleasure for me to share some of my work at SMX London this time and I am looking forward to more to come!

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

About The Author

Eun-Ji Noh is a senior data scientist working in search engine optimization and guest experience product data science at LinkedIn. She informs product decisions to help people discover LinkedIn with her data analysis and insights.

Dit artikel is vertaald van Search Engine Land

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