Google is the dominating force in the world of search engines, and there’s an entire industry dedicated to maximizing visibility within its search engine results: search engine optimization (SEO).

People like me have built their careers on finding ways to benefit from the central ranking algorithm at Google’s core. But here’s the interesting thing: Google doesn’t explicitly publish how its search algorithm works and often uses vague language when describing its updates.

So how much do we really know about Google’s ranking algorithm? And why is Google so secretive about it?

Why Google Keeps Things Secret

Google has come under fire lately, most recently by German Chancellor Angela Merkel, because it keeps its algorithm secret. Her main argument is that transparency is vitally important to maintaining a balanced society; after all, our daily searches shape our behavior in subtle and blatant ways, and not knowing the mechanisms that influence that behavior can leave us in the dark.

But Google isn’t withholding its algorithm so that it can manipulate people with reckless abandon. There are two good reasons why the company would want to keep the information a closely-guarded secret.

First, Google’s algorithm is proprietary, and it has become the dominant search competitor because of its sheer sophistication. If other competitors have free and open access to the inner workings of that algorithm, they could easily introduce a competing platform with comparable power, and Google’s search share could unfairly plummet.

Second, there are already millions of people who make a living by improving their positions within Google, and many of them are willing to use ethically questionable tactics or spam people in an effort to get more search visibility. If Google fully publishes its search algorithm, they could easily find bigger loopholes, and ruin the relatively fair search engine results pages (SERPs) we’ve come to expect from the giant.

How We Learn

So if Google withholds all the information on its algorithm, how can search optimizers know how to improve the search rankings of web pages?

  • Google revelations. Google doesn’t leave webmasters totally in the dark. While it refuses to disclose specifics about how the algorithm functions, it’s pretty open about the general intentions of the algorithm, and what webmasters can take away from it. For example, Google has published and regularly updates a guidelines manual on search quality ratings; 160 pages long, and last updated July of last year, it’s a fairly comprehensive guidebook that explains general concepts of how Google judges the quality of a given page. Google has also been known to explain its updates as they roll out—especially the larger ones—with a short summary and a list of action items for webmasters. These are all incredibly helpful sources of information.
  • Direct research. Google doesn’t give us everything, however. If you scroll through Moz’s fairly comprehensive guide on the history of Google’s algorithm changes, you’ll notice dozens of small updates that Google didn’t formally announce, and in many cases, refuses to acknowledge. How does the search community know that these algorithm changes unfolded? We have volatility indicators like MozCast, which measure how much the SERPs are changing within a given period of time; a period of high volatility is usually the signature of some kind of algorithm change. We can also conduct experiments, such as using two different tactics on two different pages and seeing which one ranks higher at the end of the experiment period. And because the SEO community is pretty open about sharing this information, one experiment is all it takes to give the whole community more experience and knowledge.
  • Experience and intuition. Finally, after several years of making changes and tracking growth patterns, you can rely a bit on your own experience and intuition. When search traffic plummets, you can usually identify a handful of potential red flags and come up with ideas for tweaks to take you back to your baseline.

What Do We Know?

So what do we really know about Google’s search algorithm?

  • The basics. We know the basic concept behind the search platform: to give users the best possible results for their queries. Google does this by presenting results that offer a combination of relevance (how appropriate the topic is) and authority (how trustworthy the source is).
  • Core ranking factors. We also know the core ranking factors that will influence your rank. Some of these come directly from Google’s webmaster guidelines, and some of them come from the results of major experiments. In any case, we have a good idea what changes are necessary to earn a high rank, and what factors could stand in your way. I covered 101 of them here.
  • Algorithm extensions and updates.We also know when there’s a new Google update, thanks to the volatility indicator, and we can almost always form a reasonable conclusion on the update’s purpose—even when Google doesn’t tell us directly.

While we still don’t know the specifics of how Google’s algorithm works—and unless the EU’s transparency campaign kicks into high gear soon, we probably won’t for the foreseeable future—we do know enough about it to make meaningful changes to our sites, and maximize our ranking potential.

Moreover, the general philosophy behind the algorithm and the basic strategies needed to take advantage of it aren’t hard to learn. If you’re willing to read Google’s documentation and learn from the experiments of others, you can get up to speed in a matter of weeks.

 Source: This article was published forbes.com By Jayson DeMers,

Categorized in Search Engine


  • Google Search finds quality of newsy content algorithmically
  • Search results to omit fake news through improved ranking signals
  • India marks 2x growth in daily active search users on Google

Google Search already receive some artificial intelligence (AI) tweaks to enhance user experience. But with the swift growth of inferior-quality content, Google is now in the process of improving the quality of its search results. VP of Engineering Shashidhar Thakur on the sidelines of Google for India 2017 on Tuesday stated that Google is making continuous efforts to cut down on the amount of fake news content listed on its search engine.

"Whether it's in India or internationally, we make sure that we uphold a high bar when it comes to the quality of newsy content. Generally, in search, we find this type of content algorithmically," Thakur told Gadgets 360. The algorithms deployed behind Google Search look for the authoritativeness of the content and its quality to rank them appropriately. Thakur said that this continuous improvement will uplift the quality of the search results over time.

"We improve ranking signals on our search engine from time to time to overcome the issue of fake news. Signals help the system understand a query or the language of the query or the text or matching different keywords to provide relevant results," explained Thakur.

Similar to other search engines that use code-based bots to crawl different webpages, Google Search indexes hundreds of billions of webpages consistently. Once indexed, Google Search adds webpages to different entries that include all the words available on those pages. This data is then processed to the Knowledge Graph that not just looks for any particular keywords but also picks user interests to give relevant results.


"Inferior-quality content on the Web isn't a new and special problem," Thakur said. "But certainly, it is a problem that we need to solve by continuous tuning and making the underlying search algorithms better. This is indeed a very crucial area of focus for us."

Google isn't the only Web company that is taking the menace of fake news seriously. Facebook and Microsoft's Bing are also testing new developments to curb fake news. A recent report by Gartner predicted that fake news will grow multifold by 2022 and people in mature economies will consume more amount of false information over the information that is true and fair.

Having said that, Google is dominating the Web space and its search engine is the most prominent area for counterfeit content. Thakur at the Google for India stage revealed the number of daily active search users in India has grown two times in the last one year. The Mountain View, California-headquartered company also released Google Go as the lightweight version of the flagship Google app on Android devices.


Source: This article was published gadgets.ndtv.com By Jagmeet Singh


Categorized in Search Engine

Editor’s note: This post is part of an ongoing series looking back at the history of Google algorithm updates. Enjoy!

Google’s Freshness, or “fresher results”, update – as the name suggests – was a significant ranking algorithm change, building on the Caffeine update, which rolled out in June 2010.

When Google announced an algorithm change on November 3, 2011, impacting ~35 percent of total searches (6-10 percent of search results to a noticeable degree), focusing on providing the user with ‘fresher, more recent search results‘, the SEO industry and content marketers alike stood up and took notice.

Where Does the Name Come From?

The freshness or ‘fresher results’ name for this algorithm update is directly taken from the official Google Inside Search blog announcement.

Google Freshness Update Nov 2011

Why Was the Freshness Update Launched?

It is predicted that more data will be created in 2017 than the previous 5,000 years of humanity, a trend which has been ongoing for a few years now, and one driving Google to act to cater for this availability and demand for up to date, fresh, new content.

When you combine this data and content growth, with the levels of new and unique queries Google handles, you begin to establish justification for identifying, handling, prioritizing and ranking fresh content within the Google search index.

According to a 2012 ReadWrite article, 16 to 20 percent of queries that get asked every day have never been asked before.

A key intention of this update is to provide greater emphasis on the importance of recentness of content specifically tied to areas like latest news, events, politics, celebrities, trends and more, specifically where the user is expected to want to know the most current information.

Someone searching for “Taylor Swift boyfriend” will likely want to know the current person she is dating, therefore content time/date stamped yesterday, with lots of social shares, engagement, and backlinks over the past few hours, will likely displace prior ranking content which has not been updated, or providing the same activity freshness signals.

Here are the results for this query as at the time of writing this article.

Tailor Swift SERPs Oct 2017

Who Was Impacted by Freshness Algorithm?

At a noticeable level, between 6 to 10 percent of search queries were impacted by the Freshness algorithm, but some degree of change was applied to a collective third (35 percent) of all searches.

One of the interesting aspects of the Freshness Algorithm update was the fact that many more sites appeared to have gained from the update, as opposed to having seen lost rankings or visibility from them. This is quite uncommon with most changes to the Google algorithm.

Looking specifically at the identified “winners” from the update, according to Searchmetrics:

Google prefers sites like news sites, broadcast sites, video portals and a lot Brand sites. This is also a type of sites which have regularly fresh content and a big brand with higher CTRs.

Industry Reaction to the Freshness Update

Due to the nature of the update being an overarching positive change; one rewarding content creators, fresh/relevant/latest news providers, and many bigger brands investing in content, the initial reaction was tied towards analysis of the change and the logical nature of the update.

The analysis of the change was associated with the expected “big” impact from the Google announcement of 35 percent of search results being affected, and the actual disproportionately small amount of negative impact being reported.

The Solution/Recovery Process

The Freshness update is one of my favorite Google algorithms as it makes perfect sense, and was impactful for changing SERPs for the better, in a logical, easy to understand, and practical way.

If you’re covering a topic area and the information you have is out of date, time-sensitive, hasn’t been refreshed or updated in some time, or is simply being surpassed by more engaging, fresh and new competing content, it is likely that you need to give that content/topic some more attention, both on page and off page.

An important part of the freshness update is that it is not just about refreshing content, but also tied to the frequency of content related to the topic.

For example; the expected frequency of content prominently ranking during a political campaign spanning weeks, would reflect the latest campaign changes rather than static (even day old) content, with since surpassed relevancy, accuracy, and associated user engagement and social sharing signals.

This update was building on Google’s established “query deserves freshness” (QDF) methodology:

THE QDF solution revolves around determining whether a topic is “hot.” If news sites or blog posts are actively writing about a topic, the model figures that it is one for which users are more likely to want current information. The model also examines Google’s own stream of billions of search queries, which Mr. Singhal believes is an even better monitor of global enthusiasm about a particular subject.

It also was made possible by Google’s Caffeine web search index update:

With Caffeine, we analyze the web in small portions and update our search index on a continuous basis, globally. As we find new pages, or new information on existing pages, we can add these straight to the index. That means you can find fresher information than ever before—no matter when or where it was published.

Practical Tactics for Recovering from the Freshness Algorithm

Five of the best ways to recover from any lost ranking (or to take advantage of the new untapped opportunity) as a result of the Freshness Algorithm change include:

1. Revisit Existing Content

Look through year on year, or even previous period content performance. Identify pages/topics that previously drove volumes of impressions, traffic, and rankings to the website, and prioritize refreshing them.

You may find that time and date stamped content in blogs, news, and media sections, have seen significant data change/drops. If this is the case, consider the value of updating the historical content by citing new sources, updating statistics, including more current quotes, and adding terms reflecting latest search queries.

2. Socially Share & Amplify Content

Social signals, fresh link signals, and associated external interest/buzz surrounding your content can fuel ranking gains tied to QDF and previous algorithm updates like the Freshness update.

Don’t underestimate the value of successful social sharing and PR activities driving new content discovery, engagement, and interaction.

3. Reconsider Content Frequency

If your website covers industry change, key events, and any degree of breaking news/insight, you may need to think about the frequency that you are informing your audience, and adding content to your website.

People are digesting more content than ever before, and users demand the latest news as it happens – minor frequency changes can make a positive difference between being first to market, or being late to the party.

4. Take a Tiered Approach to Content Creation 

With voice, video, images, virtual reality, and a host of content types, plus common website inclusion approaches (blogs, news, media, content hubs, microsites, more), adding layers of content to your digital offering will enable broader visibility of the brand on key ranking areas, plus extra  leverage of the various search verticals at your disposal.

Whether these updates result in new landing pages or adding of depth and content value to existing URLs, will differ on intent, but either way, this will support many of the freshness points relating to recovery or gains tied to this update.

5. Add Evergreen Content Into Your Content Mix 

Evergreen content is the deeper content creation that has more redundancy to the test of time, and is able to perform month in and month out, contributing to search rankings and traffic over many months, even years. Typically evergreen content reflects:

  • Thorough topical research.
  • Unique insight.
  • Targeted application of expertise on a given topic.
  • Refined content that gets updated every few months when changes require modification.
  • Longer form content (often in the several thousands of works criteria).
  • Mixed content type inclusive.

You may see this as your hero content pieces, those warranting budget, promotion, and reinvestment of time and resource.

How Successful was the Freshness Algorithm Update?

Although the Freshness Algorithm change isn’t frequently mentioned in many industry topical conversations and often gets overshadowed by the likes of Penguin, Panda, Hummingbird, Mobile First, RankBrain, and others, to me, this reinforces the level of success it had.

When you look for time intent queries like [football results] you will notice that dominant sites are providing:

  • Live scores
  • In-game updates
  • Latest results
  • Interactive scoreboards
  • Current fixtures
  • Much more

These useful and changing (often changing by the hour) results reflect the practical benefits that this update has had to our search experience, and the opportunity this brings to value-based companies, able to act on the latest data.

Freshness Myths & Misconceptions

The biggest misconception related to this algorithm update was the anticipated negative impact tied to the scale of results (~35 percent) that would be applicable to Google Freshness.

As this was one of the more positive and practical algorithm changes, the freshness update has been overlooked by many, playing the role of unsung auditor of tired, unloved content needing to be improved, and of active content use able to satisfy searcher needs, and rank for more time-sensitive user intent.

Source: This article was published searchengineland.com By Lee Wilson

Categorized in Search Engine

Still, growing frustration with rude, and even phony, online posting begs for some mechanism to filter out rubbish. So, rather than employ costly humans to monitor online discussion, we try to do it with software.

Software does some things fabulously well, but interpreting language isn’t usually one of them.

I’ve never noticed any dramatic difference in attitudes or civility between the people of Vermont and New Hampshire, yet the latest tech news claims that Vermont is America’s top source of “toxic” online comments, while its next-door neighbor New Hampshire is dead last.

Reports also claim that the humble Chicago suburb of Park Forest is trolls’ paradise.

After decades living in the Chicago Metropolitan area, I say without hesitation that the people of Park Forest don’t stand out from the crowd, for trolling or anything else. I don’t know whether they wish to stand out or not, but it’s my observation that folks from Park Forest just blend in. People may joke about Cicero and Berwyn, but not Park Forest.

So what’s going on? Software.

Perspective, a tool intended to identify “toxic” online comments, is one of the Jigsaw projects, Google experiments aimed at promoting greater safety online. Users feed it comments, and Perspective returns a 0-100 score for the percent of respondents likely to find the comment “toxic,” that is, likely to make them leave the conversation.

It was released months ago, but has drawn a blast of new publicity in the past few days since Wired used it for development of “Trolls Across America,” an article featuring an online map highlighting supposed trolling hotspots across the country.

Interpreting language is one of the most complex and subtle things that people do. The meaning of human communication is based in much more than the dictionary meaning of words. Tone of voice, situation, personal history and many other layers of context have roles to play.

The same remark may hold different significance for each person who hears it. Even one person may view a statement differently at different moments. Human language just does not lend itself to the kinds of strict rules of interpretation that are used by computers.

As soon as Perspective (which is clearly labeled as a research project) was announced, prospective users were warned about its limitations. Automated moderation was not recommended, for example. One suggested use was helping human moderators decide what to review.

David Auerbach, writing for MIT’s Technology Review, soon pointed out that “It’s Easy to Slip Toxic Language Past Alphabet’s Toxic-Comment Detector. Machine-learning algorithms are no match for the creativity of human insults.” He tested an assortment of phrases, getting results like these:

  • “‘Trump sucks’ scored a colossal 96 percent, yet neo-Nazi codeword ‘14/88’ only scored 5 percent.” [I also tested “14/88” and got no results at all. In fact, I tested all of the phrases mentioned by Auerbach and got somewhat different results, though the patterns were all similar.]
  • “Jews are human,” 72. “Jews are not human,” 64.
  • “The Holocaust never happened,” 21.

Twitter’s all atwitter with additional tests results from machine learning researchers and other curious people. Here is a sample of the phrases that were mentioned, in increasing order of toxicity scores from Perspective:

  1. I love the Führer, 8
  2. I am a man, 20
  3. I am a woman, 41
  4. You are a man, 52
  5. Algorithms are likely to reproduce human gender and racial biases, 56
  6. I am a Jew, 74
  7. You are a woman, 79

Linguistically speaking, most of these statements are just facts. If I’m a woman, I’m a woman. If you’re a man, you’re a man. If we interpret such statements as something more than neutral facts, we may be reading too much into them. “I love the Führer” is something else entirely.  To look at these scores, though, you’d get a very different impression.

The problem is, the scoring mechanism can’t be any better than the rules behind it.

Nobody at Google set out to make a rule that assigned a low toxicity score to “I love the Führer” or a high score to “I am a Jew.” The rules were created in large part through automation, presenting a crowd of people with sample comments and collecting opinions on those comments, then assigning scores to new comments based on similarity to the example comments and corresponding ratings.

This approach has limitations. The crowd of people are not without biases, and those will be reflected in the scores. And terminology not included in the sample data will create gaps in results.

A couple of years ago, I heard a police trainer tell a group of officers that removing one just word from their vocabulary could prevent 80% of police misconduct complaints filed by the public. The officers had no difficulty guessing the word. It’s deeply embedded in police jargon, and has been for so long that it got its own chapter in the 1978 law enforcement book Policing: A View from the Street.

Yet the same word credited for abundant complaints of police misconduct has appeared in at least 3 articles here on Forbes in the past month (123.), and not drawn so much as a comment.

Often, it’s not the words that offend, but the venom behind them. And that’s hard, if not impossible, to capture in an algorithm.

This isn’t to say that technology can’t do some worthwhile things with human language.

Text analytics algorithms, rules used by software to convert open-ended text into more conventional types of data, such as categories or numeric scores, can be useful. They lie at the heart of online search technology, for example, helping us find documents to topics of interest. Some other applications include:

  • e-discovery, which increases productivity for legal teams reviewing large quantities of documents for litigation
  • Warranty claim investigation, where text analysis helps manufacturers to identify product flaws early and enable corrective action
  • Targeted advertising, which uses text from content that users read or create to present relevant ads

It takes more than a dictionary to understanding the meaning of language. Context, on the page and off, is all important.

People recognize the connections between the things that people write or say, and the unspoken parts of the story. Software doesn’t do that so well.

Meta S. Brown is author of Data Mining for Dummies and creator of the Storytelling for Data Analysts and Storytelling for Tech workshops. http://www.metabrown.com.

Source: This article was published forbes.com

Categorized in Search Engine

Big data has and will change how advertisers work and businesses market.

There are plenty of words online about how big data will change every facet of our lives, and a substantial chunk of those words are devoted towards how big data will affect advertising. But instead of haphazardly leaping on the change bandwagon, advertisers need to sit down and understand what big data has changed and yet what still remains the same.

At its core, advertising is about communication as it seeks to inform consumers about a business’s product and services. But different consumers want to hear different messages, which becomes all the more important as new customers join the internet thanks to the growing popularity of mobile.

Big data can refine those messages, predict what customers want to hear with predictive analytics, and yield new insights in what customers want to hear. All of this is certainly revolutionary and will change how consumers and marketers approach advertising. But it will still be up to advertisers to create messages in the name of their clients.

Algorithms and targeting

Some things which many people do not think about as advertising are in fact a conflation of big data and marketing. Netflix is a terrific example of this. Netflix obviously does not have advertisements, but it heavily relies on algorithms to recommend shows to its viewers. These algorithms save Netflix $1 billion per year by reducing the churn rate and marketing the right shows to the right customers.

Netflix’s efforts to target consumers with the right shows is hardly unusual, as websites and online stores like YouTube, Amazon, or Steam do this all the time these days. But the key here is the reliance on algorithms to make targeting more accurate.

These algorithms require a constant stream of data to stay up to date. But now that data is everywhere. Internet users leave a constant stream of data not just on social media websites, but anywhere they go in the form of digital footprints.

This represents new opportunities and challenges for advertisers. On one hand, the digital footprints which everyone creates offers new insights to advertisers into what we truly want which can be more accurate than what we say on social media. But at the same time, advertisers do have to worry about protecting consumer privacy and security. This is not just a moral thing; advertisers or websites that are flagrantly cavalier with their user data will spark a backlash that will hurt business.

Advertising targeting has already been in place for some time now. But as advertisers collect more data, targeting will become more personalized and thus effective. Advertisers will fight not just to collect as much data as possible, but to collect data which accurately represents individual customers to market to their individual tastes.

Changing forms of advertising

Big data can uncover new information about each individual customer, but the advertiser must craft a message to appeal to said customer. But with these new insights, advertisers can entirely change how they approach marketing as they craft entirely new strategies.

This is not completely new. The rise in content marketing is often cited as a major beneficiary of big data, but content marketing as a concept is older than the Internet. Nevertheless, the rise in content marketing as well as other strategies like native advertising or the endless dance around search engine optimization.

These rising advertising strategies are fascinating because just as advertisers rely on data to craft new strategies, they give data right back to the consumer. Content marketing is all about giving consumers details about a business such as how they make food, what it is like to work there, and so on. By sharing this data, the company makes the customers feel like they are part of a group which knows common information. And in turn the customer ends up giving up his data to the company which lets it construct new advertising strategies.

This symbiosis between consumer and company shows that data is not just about cold analytics, but is about creating a bond between the two groups like all advertising sets out to do. Similarly, businesses must take the complexity of big data, analyze trends, and then create simple guidelines which their customer staff can use. All the advertising in the world will not make as big of an impression on a customer as one surly or confused customer representative.

Big data has and will change how advertisers work and businesses market to consumers through more personalized and targeted advertisement as well as creating new forms of advertising. But big data is less important than smart data and strategy. Business leaders who can break big data down into small chunks, come up with a smart strategy, and formulate an effective message will still thrive just as much as they would have in the past. In this way, big data is not quite the revolutionary change that many think.

This article is published as part of the IDG Contributor Network. Want to Join?

Categorized in Search Engine

When Netflix recommends you watch “Grace and Frankie” after you’ve finished “Love,” an algorithm decided that would be the next logical thing for you to watch. And when Google shows you one search result ahead of another, an algorithm made a decision that one page was more important than the other. Oh, and when a photo app decides you’d look better with lighter skin, a seriously biased algorithm that a real person developed made that call.

Algorithms are sets of rules that computers follow in order to solve problems and make decisions about a particular course of action. Whether it’s the type of information we receive, the information people see about us, the jobs we get hired to do, the credit cards we get approved for, and, down the road, the driverless cars that either see us or don’t see us, algorithms are increasingly becoming a big part of our lives.

But there is an inherent problem with algorithms that begins at the most base level and persists throughout its adaption: human bias that is baked into these machine-based decision-makers.

You may remember that time when Uber’s self-driving car ran a red light in San Francisco, or when Google’s photo app labeled images of black people as gorillas. The Massachusetts Registry of Motor Vehicles’ facial-recognition algorithm mistakenly tagged someone as a criminal and revoked their driver’s license. And Microsoft’s bot Tay went rogue and decided to become a white supremacist. Those were algorithms at their worst. They have also recently been thrust into the spotlight with the troubles around fake news stories surfacing in Google search results and on Facebook.

But algorithms going rogue have much greater implications; they can result in life-altering consequences for unsuspecting people. Think about how scary it could be with algorithmically biased self-driving cars, drones and other sorts of automated vehicles. Consider robots that are algorithmically biased against black people or don’t properly recognize people who are not cisgender white people, and then make a decision on the basis that the person is not human.

Another important element to consider is the role algorithm’s play in determining what we see in the world, as well as how people see us. Think driverless cars “driven” by algorithms mowing down black people because they don’t recognize black people as human. Or algorithmic software that predicts future criminals, which just so happens to be biased against black people.

A variety of issues can arise as a result of bad or erroneous data, good but biased data because there’s not enough of it, or an inflexible model that can’t account for different scenarios.

The dilemma is figuring out what to do about these problematic algorithmic outcomes. Many researchers and academics are actively exploring how to increase algorithmic accountability. What would it mean if tech companies provided their code in order to make these algorithmic decisions more transparent? Furthermore, what would happen if some type of government board would be in charge of reviewing them?

Whatever approach is taken to ensure bias is removed from the development of algorithms, it can’t dramatically impede progress, DJ Patil, former chief data scientist of the U.S., tells me. Solutions can only be implemented, and therefore effective, if tech companies fully acknowledge their roles in maintaining and perpetuating bias, discrimination and falsehoods, he adds.

If you think about the issues we faced a few years ago versus the issues we face now, they are compounding, he adds. “So how do we address that challenge?” In developing new technologies, there needs to be more diversity on the team behind these algorithms. There’s no denying that, Patil says, but the issue is scalability across the whole realm of diversity.

“There’s diversity of race, there’s diversity of religion, there’s diversity with respect to disability. The number of times I’ve seen somebody design something where if they made a slight decision choice you could design for a much broader swath of society — just so easy, just to make a slight design change, but they just didn’t know. And I think one of the challenges is that we don’t have scalable templates to do that.”

Google, for example, determines what many people see on the internet. As Frank Pasquale writes in his book, “The Black Box Society: The Secret Algorithms That Control Money and Information,” Google, as well as other tech companies, set the standards by which all of us are judged, but there’s no one really judging them (141, Pasquale).

When you conduct a Google Image search for “person,” you don’t see very many people of color, which perpetuates the normalization of whiteness and reinstills biases around race. Instead, you’ll see many pictures of white men, says Sorelle Friedler, affiliate at the Data & Society Research Center and ex-Googler who worked on X and search infrastructure.

“That is perhaps representative of the way that ‘person’ is broadly used in our society, unfortunately,” Friedler says. “So then the question is, is it appropriate for that sort of linguistic representation to make its way to image search? And then Google would need to decide, am I okay with black people only being represented only if you search specifically for black people? And I think that that’s a philosophical decision about what we want our society to look like, and I think it’s one that’s worth reckoning with.”

Perhaps Google doesn’t see itself as having a big responsibility to intervene in a situation like this. Maybe the argument of “it’s a result of the things our users are inputting” is acceptable in this scenario. But search queries related to the Holocaust or suicide have prompted Google to intervene.

Algorithms determine Google’s search results and suggestions. Some of Google’s algorithmic fails are more egregious than others, and sometimes Google steps in, but it often doesn’t.

“If you search for ways to kill yourself, you’re directed toward a suicide hotline,” Robyn Caplan, a research analyst at Data & Society, tells TechCrunch. “There are things Google has deemed relevant to the public interest that they’re willing to kind of intervene and guard against, but there really isn’t a great understanding of how they’re assessing that.”

Earlier this year, if you searched for something like, “is the Holocaust real?,” “did the holocaust happen” or “are black people smart?” one of the first search results for both queries was pretty problematic. It wasn’t until people expressed outrage that Google decided to do something.

“When non-authoritative information ranks too high in our search results, we develop scalable, automated approaches to fix the problems, rather than manually removing these one-by-one,” a Google spokesperson tells TechCrunch via email. “We are working on improvements to our algorithm that will help surface more high-quality, credible content on the web, and we’ll continue to improve our algorithms over time in order to tackle these challenges.”

In addition to its search results and suggestions, Google’s photo algorithms and the ads it serves have also been problematic. At one point, Google’s photo algorithm mistakenly labeled black people as gorillas.

Google launched Photos in May 2015 to relatively good reception. But after developer Jacky Alciné pointed out the flaw, Bradley Horowitz, who led Google Photos at the time, said his inbox was on fire.

“That day was one of the worst days of my professional life, maybe my life,” Horowitz said in December.

“People were typing in gorilla and African-American people were being returned in the search results,” Horowitz said. How that happened, he said, was that there was garbage going in and garbage going out — a saying he said is common in computer science. “To the degree that the data is sexist or racist, you’re going to have the algorithm imitating those behaviors.”

Horowitz added that Google’s employee base isn’t representative of the users it serves. He admitted that if Google had a more diverse team, the company would have noticed the problems earlier in the development process.

Another time, Google featured mugshots at the top of search results for people with “black-sounding” names. Latanya Sweeney, a black professor in government and technology at Harvard University and founder of the Data Privacy Lab, brought this to the public’s attention in 2013 when she published her study of Google AdWords. She found that when people search Google for names that traditionally belong to black people, the ads shown are of arrest records and mugshots.

What’s driving mistakes like this is the idea that the natural world and natural processes are just like the social world and social processes of people, says Pasquale.

“And it’s this assumption that if we can develop an algorithm that picks out all of the rocks as rocks correctly, we can have one that classifies people correctly or in a useful way or something like that,” Pasquale says. “I think that’s the fundamental problem. They are taking a lot of natural science methods and throwing them into social situations and they’re not trying to tailor the intervention to reflect human values.”

When an algorithm produces less-than-ideal results, it could be that the data set was bad to begin with, the algorithm wasn’t flexible enough, the team behind the product didn’t fully think through the use cases, humans interacted with the algorithm enough to manipulate it, or even all of the above. But no longer are the days where tech companies can just say, ‘Oh, well it’s just an app” or “Oh, we didn’t have the data,” Patil says. “We have a different level of responsibility when you’re designing a product that really impacts people’s lives in the way that it can.”

While algorithms also have vast potential to change our world, Google’s aforementioned fails are indicative of a larger issue: the algorithm’s role in either sustaining or perpetuating historic models of discrimination and bias or spreading false information.

“There is both the harm data can do and the incredible opportunity it has to help,” Patil says. “We often focus on the harm side and people talk about the way math is — we should be scared of it and why we should be so afraid of it.

“We have to remember these algorithms and these techniques are going to be the way we’re going to solve cancer. This is how we’re going to cure the next form of diseases. This is how we’re going to battle crises like Ebola and Zika. Big data is the solution.”

Abarrier to tackling algorithmic issues that pertain to content on the internet is Section 230 of the Communications Decency Act, which states, “No provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.”

It made it possible for tech companies to scale because it relieved platforms of any responsibilities for dealing with illegal or objectionable conduct of its users. The Electronic Frontier Foundation calls it “one of the most valuable tools for protecting freedom of expression and innovation on the internet.”

If this law didn’t exist, we could essentially deputize Google and other tech companies to operate as censors for what we consider to be objectionable speech. Something like that is happening in Europe with The Right to be Forgotten.

“American scholars and policy people are somewhat terrified because basically what has happened there is practically speaking, Google has become the arbiter of these claims,” says Solon Barocas, a postdoc researcher at the NYC Lab of Microsoft Research and member of the Society, Ethics, and AI group. “It’s not like a government agency is administering the decisions of what should be taken down. Instead, it said ‘Google you have a responsibility to do this’ and then Google does it themselves. That has frightened a lot of Americans.”

But given the existence of Section 230 of the CDA and the fact that it provides many protections for platforms, it may be difficult to use legislative means in the U.S. to affect what content is trending over Facebook or what search results appear on Google.

Outside the U.S., however, legislation could affect the way these tech companies operate inside America, Caplan says. In Germany, for example, the government has drafted a law that would fine social networks up to 50 million euro for failing to remove fake news or hate speech.

Meanwhile, the European Union’s digital chief, Andrus Ansip, warned Facebook earlier this year that while he believes in self-regulatory measures, he’s ready to take legislative action if it comes to that.

“What we’ve seen in the past is that these types of policies that take place outside of the U.S. do have a pretty big role in shaping how information is structured here,” Caplan says. “So if you look at Google’s autocomplete algorithm, you see a similar thing — that different auto-completions aren’t allowed because of libel cases that happened abroad, even though Google is protected here. Those kinds of policies proposed by countries with a clear understanding of what they’re willing to regulate media-wise may have an interesting impact here.”

Even if Section 230 stays in place, and it most likely will, there are ways to reevaluate and reprogram algorithms to make better decisions and circumvent potential biases or discriminatory outcomes before they happen.

While there needs to be more diversity on the teams developing software in order to truly take into account the different number of scenarios an algorithm may have to deal with, there’s no straightforward, cut-and-dried solution to every company’s algorithmic issues. But researchers have proposed several potential methods to address algorithmic accountability.

Two areas developing rapidly are related to the front- and backend process, respectively, Barocas tells me. The front-end method involves ensuring certain values are encoded and implemented in the algorithmic models that tech companies build. For example, tech companies could ensure that concerns of discrimination and fairness are part of the algorithmic process.

“Making sure there are certain ideas of fairness that constrain how the model behaves and that can be done upfront — meaning in the process of developing that procedure, you can make sure those things are satisfied.”

On the backend, you could imagine that developers build the systems and deploy them without being totally sure how they will behave, and unable to anticipate the potential adverse outcomes they might generate. What you would do, Barocas says, is build the system, feed it a bunch of examples, and see how it behaves.

Let’s say the system is a self-driving car and you feed it examples of pedestrians (such as a white person versus a black person versus a disabled person). By analyzing how the system operates based on a variety of inputs/examples, one could see if the process is discriminatory. If the car only stops for white people but decides to hit black and disabled people, there’s clearly a problem with the algorithm.

“If you do this enough, you can kind of tease out if there’s any type of systematic bias or systematic disparity in the outcome, and that’s also an area where people are doing a lot of work,” Barocas says. “That’s known as algorithmic auditing.”

When people talk about algorithmic accountability, they are generally talking about algorithmic auditing, of which there are three different levels, Pasquale says.

“In terms of algorithmic accountability, a first step is transparency with respect to data and algorithms,” Pasquale says. “With respect to data, we can do far more to ensure transparency, in terms of saying what’s going into the information that’s guiding my Facebook feed or Google search results.”

So, for example, enabling people to better understand what’s feeding their Facebook news feeds, their Google search results and suggestions, as well as their Twitter feeds.

“A very first step would be allowing them to understand exactly the full range of data they have about them,” Pasquale says.

The next step is something Pasquale calls qualified transparency, where people from the outside inspect and see if there’s something untoward going on. The last part, and perhaps most difficult part, is getting tech companies to “accept some kind of ethical and social responsibility for the discriminatory impacts of what they’re doing,” Pasquale says.

The fundamental barrier to algorithmic accountability, Pasquale says, is that until we “get the companies to invest serious money in assuring some sort of both legal compliance and broader ethical compliance with personnel that have the power to do this, we’re not really going to get anywhere.”

Pasquale says he is a proponent of government regulation and oversight and envisions something like a federal search commission to oversee search engines and analyze how they rank and rate people and companies.

Friedler, however, sees a situation in which an outside organization would develop metrics that measure what they consider to be the problem. Then that organization could publicize those metrics and its methodology.

“As with many of these sorts of societal benefits, it’s up to the rest of society to determine what we want to be seeing them do and then to hold them accountable,” Friedler tells me. “I also would like to believe that many of these tech companies want to do the right thing. But to be fair, determining what the right thing is is very tricky. And measuring it is even trickier.”

Algorithms aren’t going to go away, and I think we can all agree that they’re only going to become more prevalent and powerful. But unless academics, technologists and other stakeholders determine a concrete process to hold algorithms and the tech companies behind them accountable, we’re all at risk.

This article was  published in techcrunch.com by  Megan Rose Dickey

Categorized in Search Engine

Google is in the process of revamping its existing search algorithm to curb the promotion of extreme views, conspiracy theories and, most importantly, fake news.

The internet giant that has an internal and undisclosed ranking for websites and their URLs said it will demote "low-quality" websites especially those circulating misleading or fake content. A group of 10,000-plus staff, known as "raters" will assess search results and flag web pages that host hoaxes, conspiracy theories and content that is sub-par.

"In a world where tens of thousands of pages are coming online every minute of every day, there are new ways that people try to game the system," Google's Ben Gomes said in a blog post. "In order to have long-term and impactful changes, more structural changes [to Google's search engine] are needed."

Check out some of the major changes Google has made public regarding its algorithm change:

  • Users can now report offensive suggestions from the Autocomplete feature and false statements in Google's Direct Answer box, which will be manually checked by a moderator.
  • Users can even flag content that appears on Featured Snippets in search
  • Instead of bots that have been traditionally used by search companies, Google assures real people will assess the quality of Google's search results
  • Low-quality web pages with content of conspiracy theories, extremism and unreliable sources will be demoted in ranking
  • More authoritative pages with strong sources and facts will be rated higher
  • Linking to offending websites and hiding text on a page that is invisible to humans, but visible to the search algorithms can also demote a webpage
  • Suspicious files and formats not recognised on landing pages which the company warns is malware in many cases

For a detailed explanation on how the company determines its search and rankings check out its search quality evaluation guidelines that has been updated. The company that has been secretive about its search strategy in the past, has now promised more transparency to let people know how the business works after coming under fire for failing to combat fake and extremist content.

Source : ibtimes.co.uk by Agamoni Ghosh

Categorized in Search Engine

In a bid to fight fake news and low-quality content, Google is updating its search algorithms. In addition to making improvements to search ranking, the search engine giant wants to offer people easier ways to directly report offensive or misleading content.

In a blog post, Google Vice President of Search Ben Gomes said that Google has improved its evaluation methods and made algorithmic updates to surface more authoritative content.

For the first time, users will be able to directly flag content that appears in Autocomplete and Featured Snippets in Google Search.

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Autocomplete helps predict the searches people might be typing, while Featured Snippets appear at the top of search results showing a highlight of the information relevant to what people are looking for.

“Today, in a world where tens of thousands of pages are coming online every minute of every day, there are new ways that people try to game the system. The most high profile of these issues is the phenomenon of “fake news,” where content on the web has contributed to the spread of blatantly misleading, low quality, offensive or downright false information,” Gomes said in the blog.

Google has a team of evaluators – real people – to monitor the quality of Google’s search results. Their ratings will help the company gather data on the quality of its results and identify areas for improvements.

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Last month, Google updated its Search Quality Rater Guidelines to provide more detailed examples of low-quality web pages for raters to appropriately flag, which can include misleading information, unexpected offensive results, hoaxes and unsupported conspiracy theories. “These guidelines will begin to help our algorithms in demoting such low-quality content and help us to make additional improvements over time,” Gomes said.

Featured Snippets

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Meanwhile, Google recently updated its How Search Works site to provide detailed info to users and website owners about the technology behind Google Search.

This article was published in marketexclusive.com by David Zazoff


Categorized in Search Engine

Two billion photos find their way onto Facebook’s family of apps every single day and the company is racing to understand them and their moving counterparts with the hope of increasing engagement. And while machine learning is undoubtedly the map to the treasure, Facebook and its competitors are still trying to work out how to deal with the spoils once they find them.

Facebook AI Similarity Search (FAISS), released as an open-source library last month, began as an internal research project to address bottlenecks slowing the process of identifying similar content once a user’s preferences are understood. Under the leadership of Yann LeCun, Facebook’s AI Research (FAIR) lab is making it possible for everyone to more quickly relate needles within a haystack.

On its own, training a machine learning model is already an incredibly intensive computational process. But a funny thing happens when machine learning models comb over videos, pictures and text — new information gets created! FAISS is able to efficiently search across billions of dimensions of data to identify similar content.

In an interview with TechCrunch, Jeff Johnson, one of the three FAIR researchers working on the project, emphasized that FAISS isn’t so much a fundamental AI advancement as it is a fundamental AI-enabling technique.

Imagine you wanted to perform object recognition on a public video that a user shared to understand its contents so you could serve up a relevant ad. First you’d have to train and run that algorithm on the video, coming up with a bunch of new data.

From that, let’s say you discover that your target user is a big fan of trucks, the outdoors and adventure. This is helpful, but it’s still hard to say what advertisement you should display — a rugged tent? An ATV? A Ford F-150?

To figure this out, you would want to create a vector representation of the video you analyzed and compare it to your corpus of advertisements with the intent of finding the most similar video. This process would require a similarity search, whereby vectors are compared in multi-dimensional space.

In this representation of a similarity search, the blue vector is the query. The distance between the “arrows” reflects their relative similarity.

In real life, the property of being an adventurous outdoorsy fan of trucks could constitute hundreds or even thousands of dimensions of information. Multiply this by the number of different videos you’re searching across and you can see why the library you implement for similarity search is important.

“At Facebook we have massive amounts of computing power and data and the question is how we can best take advantage of that by combining old and new techniques,” posited Johnson.

Facebook reports that implementing k-nearest neighbor across GPUs resulted in an 8.5x improvement in processing time. Within the previously explained vector space, nearest neighbor algorithms let us identify the most closely related vectors.

More efficient similarity search opens up possibilities for recommendation engines and personal assistants alike. Facebook M, its own intelligent assistant, relies on having humans in the loop to assist users. Facebook considers “M” to be a test bed to experiment with the relationship between humans and AI. LeCun noted that there are a number of domains within M where FAISS could be useful.

“An intelligent virtual assistant looking for an answer would need to look through a very long list,” LeCun explained to me. “Finding nearest neighbors is a very important functionality.”

Improved similarity search could support memory networks to help keep track of context and basic factual knowledge, LeCun continued. Short-term memory contrasts with learned skills like finding the optimal solution to a puzzle. In the future, a machine might be able to watch a video or read a story and then answer critical follow-up questions about it.

More broadly, FAISS could support more dynamic content on the platform. LeCun noted that news and memes change every day and better methods of searching content could drive better user experiences.

Two billion new photos a day presents Facebook with a billion and a half opportunities to better understand its users. Each and every fleeting chance at boosting engagement is dependent on being able to quickly and accurately sift through content and that means more than just tethering GPUs.

Source : techcrunch.com

Categorized in Social

As Google becomes increasingly sophisticated in its methods for scoring and ranking web pages, it's more difficult for marketers to keep up with SEO best practices. Columnist Jayson DeMers explores what can be done to keep up in a world where machine learning rules the day.

Google’s rollout of artificial intelligence has many in the search engine optimization (SEO) industry dumbfounded. Optimization tactics that have worked for years are quickly becoming obsolete or changing.

Why is that? And is it possible to find a predictable optimization equation like in the old days? Here’s the inside scoop.

The old days of Google

Google’s pre-machine-learning search engine operated monolithically. That is to say, when changes came, they came wholesale. Large and abrupt movements, sometimes tectonic, were commonplace in the past.

What applied to one industry/search engine result applied to all results. This was not to say that every web page was affected by every algorithmic change. Each algorithm affected a specific type of web page. Moz’s algorithm change history page details the long history of Google’s algorithm updates and what types of sites and pages were impacted.

The SEO industry began with people deciphering these algorithm updates and determining which web pages they affected (and how). Businesses rose and fell on the backs of decisions made due to such insights, and those that were able to course-correct fast enough were the winners. Those that couldn’t learned a hard lesson.

These lessons turned into the “rules of the road” for everyone else, since there was always one constant truth: algorithmic penalties were the same for each vertical. If your competitor got killed doing something Google didn’t like, you’d be sure that as long as you didn’t commit the same mistake, you’d be OK. But recent evidence is beginning to show that this SEO idiom no longer holds. Machine learning has made these penalties specific to each keyword environment. SEO professionals no longer have a static set of rules they can play by.

Dr. Pete Meyers, Moz’s Marketing Scientist recently noted, “Google has come a long way in their journey from a heuristic-based approach to a machine learning approach, but where we’re at in 2016 is still a long way from human language comprehension. To really be effective as SEOs, we still need to understand how this machine thinks, and where it falls short of human behavior. If you want to do truly next-level keyword research, your approach can be more human, but your process should replicate the machine’s understanding as much as possible.”

Moz has put together guides and posts related to understanding Google’s latest artificial intelligence in its search engine as well as launched its newest tool, Keyword Explorer, which addresses these changes.

Google decouples ranking updates

Before I get into explaining how things went off the rails for SEOs, I first have to touch on how technology enabled Google’s search engine to get to its current state.

It has only been recently that Google has possessed the kind of computational power to begin to make “real-time” updates a reality. On June 18, 2010, Google revamped its indexing structure, dubbed “Caffeine,” which allowed Google to push updates to its search index quicker than ever before. Now, a website could publish new or updated content and see the updates almost immediately on Google. But how did this work?

Google - caffeine updates

Before the Caffeine update, Google operated like any other search engine. It crawled and indexed its data, then sent that indexed data through a massive web of SPAM filters and algorithms that determined its eventual ordering on Google’s search engine results pages.

After the Caffeine update, however, select fresh content could go through an abbreviated scoring process (temporarily) and go straight to the search results. Minor things, like an update to a page’s title tag or meta description tag, or a published article for an already “vetted” website, would be candidates for this new process.

Sounds great, right? As it turned out, this created a huge barrier to establishing correlation between what you changed on your website and how that change affected your ranking. The detaching of updates to its search results — and the eventual thorough algorithmic scoring process that followed — essentially tricked many SEOs into believing that certain optimizations had worked, when in fact they hadn’t.

This was a precursor to the future Google, which would no longer operate in a serialized fashion. Google’s blog effectively spelled out the new Caffeine paradigm: “[E]very second Caffeine processes hundreds of thousands of pages in parallel.”

From an obfuscation point of view, Caffeine provided broad cover for Google’s core ranking signals. Only a meticulous SEO team, which carefully isolated each and every update, could now decipher which optimizations were responsible for specific ranking changes in this new parallel algorithm environment.

When I reached out to him for comment, Marcus Tober, founder and CTO of Searchmetrics, said, “Google now looks at hundreds of ranking factors. RankBrain uses machine learning to combine many factors into one, which means factors are weighted differently for each query. That means it’s very likely that even Google’s engineers don’t know the exact composition of their highly complex algorithm.”

“With deep learning, it’s developing independently of human intervention. As search evolves, our approach is evolving with Google’s algorithmic changes. We analyze topics, search intention and sales funnel stages because we’re also using deep learning techniques in our platform. We highlight content relevance because Google now prioritizes meeting user intent.”

These isolated testing cycles were now very important in order to determine correlation, because day-to-day changes on Google’s index were not necessarily tied to ranking shifts anymore.

The splitting of the atomic algorithm

As if that weren’t enough, in late 2015, Google released machine learning within its search engine, which continued to decouple ranking changes from its standard ways of doing things in the past.

As industry veteran John Rampton reported in TechCrunch, the core algorithms within Google now operate independently based on what is being searched for. This means that what works for one keyword might not work for another. This splitting of Google’s search rankings has since caused a tremendous amount of grief within the industry as conventional tools, which prescribe optimizations indiscriminately across millions of keywords, could no longer operate on this macro level. Now, searcher intent literally determines which algorithms and ranking factors are more important than others in that specific environment.

This is not to be confused with the recent announcement that there will be a separate index for Mobile vs. Desktop, where a clear distinction of indexes will be present. There are various tools to help SEOs understand their place within separate indexes. But how do SEOs deal with different ranking algorithms within the same index?

The challenge is to categorize and analyze these algorithmic shifts on a keyword basis. One technology that addresses this — and is getting lots of attention — was invented by Carnegie Mellon alumni Scott Stouffer. After Google repeatedly attempted to hire him, Stouffer decided instead to co-found an AI-powered enterprise SEO platform called Market Brew, based on a number of patents that were awarded in recent years.

Stouffer explains, “Back in 2006, we realized that eventually machine learning would be deployed within Google’s scoring process. Once that happened, we knew that the algorithmic filters would no longer be a static set of SEO rules. The search engine would be smart enough to adjust itself based on machine learning what worked best for users in the past. So we created Market Brew, which essentially serves to ‘machine learn the machine learner.'”

“Our generic search engine model can train itself to output very similar results to the real thing. We then use these predictive models as a sort of ‘Google Sandbox’ to quickly A/B test various changes to a website, instantly projecting new rankings for the brand’s target search engine.”

Because Google’s algorithms work differently between keywords, Stouffer says there are no clear delineations anymore. Combinations of keyword and things like user intent and prior success and failure determine how Google weights its various core algorithms.

Predicting and classifying algorithmic shifts

Is there a way we, as SEOs, can start to quantitatively understand the algorithmic differences/weightings between keywords? As I mentioned earlier, there are ways to aggregate this information using existing tools. There are also some new tools appearing on the market that enable SEO teams to model specific search engine environments and predict how those environments are shifting algorithmically.

A lot of the answers depend on how competitive and broad your keywords are. For instance, a brand that only focuses on one primary keyword, with many variations of subsequent long-tail keyword phrases, will likely not be affected by this new way of processing search results. Once an SEO team figures things out, they’ve got it figured out.

On the flip side, if a brand has to worry about many different keywords that span various competitors in each environment, then investment in these newer technologies may be warranted. SEO teams need to keep in mind that they can’t simply apply what they’ve learned in one keyword environment to another. Some sort of adaptive analysis must be used.


Technology is quickly adapting to Google’s new search ranking methodology. There are now tools that can track each algorithmic update, determining which industries and types of websites are affected the most. To combat Google’s new emphasis on artificial intelligence, we’re now seeing the addition of new search engine modeling tools that are attempting to predict exactly which algorithms are changing, so SEOs can adjust strategies and tactics on the fly.

We’re entering a golden age of SEO for engineers and data scientists. As Google’s algorithms continue to get more complex and interwoven, the SEO industry has responded with new high-powered tools to help understand this new SEO world we live in.

Author : Jayson DeMers

Source : searchengineland.com

Categorized in Search Engine
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