Living Real-Time Means It Is Now Or Never

I used to be a meticulous online bookmarker since the 90s. I used Yahoo! Bookmarks, then switched to Delicious (before it got acquired by Yahoo!), moving to local browser-based bookmarks, and then on to experimenting with Faviki, Diigo, and Pearltrees. After all these years, I am realizing that I don’t really use bookmarks in their true sense anymore.

Working in the field of technology news 24×7, I am consuming more information than ever before. How do I keep up with this real-time information firehose? That’s what I’d like to share in this post.

Background

Social Bookmarking – The Land Time Forgot by my friend Keith initially inspired this post, followed by the recent Does Anyone Bookmark Anymore? from the Sysomos Blog.

First, let me distinguish between two kinds of bookmarks.

Short-Term Bookmarking: Read It Later

This is epitomized by services like Instapaper and ReadItLater. You use these services when you come across something interesting, don’t have the time to read it immediately, and save it for reading later.

This is necessitated by our 24×7 online lifestyle, where we are more at leisure to read things on our favorite device screens at specific periods during the day.

Long-Term Bookmarking: Read, Like, Save for Reference

This is the traditional bookmark where you want to save a link for future reference because you liked it, and want to be able to reference it later.

For example, here is a Pearltree I created when Facebook announced its Privacy Changes last year:

Facebook Privacy

 

Today, I use neither of the above two kinds of bookmarks.

Read It Now, Or Never

I do not use any service that lets me save a link that I can read later. Because, by the time ‘later’ comes, there is already plenty of new stuff for me to check out.

Living real-time means it is always now or never.

What happens when there’s a really long article, say an op-ed, that’s making the rounds? I use speed-reading techniques, like these, to absorb the gist as best as I can, before moving on. New tools like tldr.it are coming up to summarize long articles for you.

Another factor is that if a really long article is really that important, it will be referenced, shared, and discussed in your own personalized online world sufficiently enough that you will have ample opportunities to read it later. As Mathew Ingram put it in 2008, “if the news is important, it will find me”.

Google Instant Search Works Better Than Bookmarks

Google’s stated mission is “to organize the world’s information and make it universally accessible and useful”. If bookmarks were the most efficient way of finding information for reference, you would think that Google would have produced the best bookmarking service on the web by now!

Have you read anything of the kind “Google plans to kill Delicious”, “Google introduces new service to obliterate Diigo/XMarks/etc.”? Probably not. Because, in my experience, Google Instant Search is far, far more efficient than any bookmarking service out there.

That is the reason why Google has not invested heavily in its obscure bookmarking service, but rather focused on improving its search engine and make it more efficient.

I am not exaggerating. You will find dozens of carefully curated links in most of my lengthy blog posts, but not a single one of them was bookmarked. All of them were instantly looked up in the process of my writing, without interrupting the flow.

What About Digital Memories?

In Timeless vs. Real Time, I waxed lyrically about the waning shelf-life of digital artifacts. My philosophy about stuff that is personally important to me is simple: I save it locally on my own media.

If there is any article, photo, music, or video that you would really like to preserve, don’t use any online service or social network to preserve it. Online web services may or may not remain in business, may or may not allow you to port data. Why take the risk?

Disclaimer

Finally, this post is not intended to suggest that everyone ditch bookmarking. Everyone’s requirements from their online lives are different. This post is about how I live in real-time, which may not be what everyone would like to do. What I do want to stress is that with this approach, I am able to consume far more information than I did previously, more efficiently.

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Facebook’s Main Enemy Is Not Google; It Is Email

A recent research survey revealed that:

  • Over 66% of web users share content with friends and family, with 50% doing it at least once a week.
  • 86% of respondents still used email to share content, while only 49% said they used Facebook.
  • For ages 18-24, 76% said they used Facebook to share content, compared with 70% via email

For all the tech press that the Facebook vs. Google battle receives, I think this is a more fundamental battle that is key to Facebook in the long term.

Why?

Email Is Private

Gmail’s famed creator Paul Buchheit has been with Facebook for over a year. We have not seen any noteworthy feature enhancement to Facebook’s internal messaging system for a long time. They have introduced Places, Groups, high-res Photos, and a host of other enhancements, but nothing for messaging.

This is because private messages between people are explicitly private. There is no social element involved that can be legitimately captured. Remember the Gmail targeted ad controversy? Facebook has already learnt that lesson, thanks to Google.

Email Bypasses Facebook

Email works with standard POP3/IMAP protocols and is interoperable between platforms, services, and devices from various vendors. Emails sent between web users of these different services offer no value for Facebook. In fact, Email bypasses Facebook altogether and therein lies the battle.

Facebook wants to know when you Like any content on the web. Facebook wants to be the place where you go to share content you Like. The Facebook Like button is intended to replace the Email Send button.

The Future Is Public Social Sharing

Who will win this battle? Web user behavior is largely turning to public social sharing. Emails are being reduced largely to notifications and quick messages, rather than any real content sharing. It isn’t so difficult to see where we’re headed. Just ask the 18-24 year olds.

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What do you do when you make an embarrassing mistake on the social web? I have been seeing different behaviors and wanted to pen my thoughts on how your behavior affects the trust others place in you.

Yesterday, a well-known person in the tech world tweeted a screenshot about what she thought was a new feature in Gmail. After I pointed out that it probably wasn’t a new feature at all, she simply deleted her tweet.

At the same time, a tech blog picked up her tweet and wrote a blog post describing this ‘new’ Gmail feature. After I @replied the blogger, rather than updating the post, they deleted the post entirely. (They later reinstated the post after I publicly voiced my disappointment).

Many months earlier, the same person had retweeted a TechCrunch tweet that had a sensational headline, but a bad URL link. It was obvious that she had retweeted it without even clicking on the link. After realizing what had happened, she simply deleted her retweet.

Contrast this with the following examples.

Yesterday, a prominent Indian celebrity’s Twitter account was hacked, and it started tweeting malicious URLs. Others started retweeting these with comments about it being hacked.

As soon as a friend I follow discovered that these URLs were malicious, she deleted her old-style retweet. But after that, she tweeted publicly that she was doing so to avoid others clicking on that link.

Couple of days back, I wrote a post about Schmidt’s comments apparently disappearing from a WSJ article. It was soon brought to my attention that the comments were indeed there, and another news story was merged with the original one, which had caused the confusion.

Within seconds, I scrambled to update the post, tweet that it was a mistake on my part, and thank the person who pointed it out both in the post and on Twitter. The thought of deleting the post entirely never even crossed my mind.

There are several other instances I have seen on FriendFeed, where a few people made rude comments about someone. In some cases, they apologized in later comments, in others, they simply deleted the rude remarks. In the case of the former, the relationships healed, in the latter, they were permanently estranged.

There are numerous such examples all of us encounter in the social web. The different behaviors I’ve seen fall under three broad categories:

  1. Delete any instances revealing the mistake and say nothing about it publicly.
  2. Delete any instances revealing the mistake, and thank the person who pointed it out privately.
  3. Retain the evidence of your mistake, and publicly thank the person who pointed it out.

Most people I’ve observed practice either #1 or #2. They hope that in this world of inter-connected networks, their cross-posted tweets and comments that are auto-posted and shared across a multitude of other networks won’t be seen by the majority of their followers. These practices avoid the public embarrassment, while apparently retaining their trust and influence with those who didn’t discover the mistake.

On the other hand, virtually all the people whom I trust and respect the most in the online world, follow #3. Why?

Because not only do they retain their trust and influence, they actually enhance it by their public admission and expression of gratitude. They know and accept that to err is human. Their public admission shows all their followers that their word can be trusted. Their public expression of gratitude reveals that they listen to their followers and are ready to admit their mistakes.

They convert an embarrassing “Oops!” moment into an opportunity to build their trust. What do you think? Which of the three categories of behavior do you think is the best?

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Update: It seems the WSJ has combined and merged two different stories. The quote from Schmidt is still there, though way below in the new merged copy. Thanks, Henry Blodget!

Yesterday, I wrote about Schmidt’s comments reported by the WSJ on wanting users’ Facebook contact lists. The post from WSJ appeared on Techmeme:

Techmeme Snapshot of WSJ

The quote from Schmidt in question, led to headlines like these, from Fortune:

Techmeme Snapshot of Fortune

Except, if you visit that WSJ story, it has been replaced with an entirely different one:

New WSJ Story

What’s up?

I tried searching for the old story on WSJ.com, but it’s not available anymore. I tried the cached version of that page from all search engines but couldn’t get the old story.

Strangely, Reuters reporting on the same event did not have the same quote from Schmidt that the WSJ had:

“The best thing that would happen is for Facebook to open up its data,” Mr. Schmidt said. “Failing that, there are other ways to get that information.” He declined to be specific.

At present, both the WSJ and Mr. Schmidt can deny these reports.

The only place I can find this story, with that quote, is on Voices on All Things Digital, which gets a syndicated feed from the WSJ. And in case that too disappears, here’s a snapshot for proof:

Schmidt Quote from WSJ

It is certainly strange that the Wall Street Journal should replace an old story with a new one with the exact same URL. Is there something going on that we are not supposed to know about?

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From the Google Zeitgeist conference Tuesday, the WSJ reports:

Mr. Schmidt said Google hoped to at least get access to Facebook users’ contact lists so that people can grow their social network on Google. He said, without elaborating, that Google’s products would incorporate more social-networking elements later this year.

"The best thing that would happen is for Facebook to open up its data," Mr. Schmidt said. "Failing that, there are other ways to get that information." He declined to be specific.

In other words, Google is now admitting that it wants access to Facebook’s social graph.

A Mess Of Multiple Social Graphs

Consider the implications of this admission. At present, Google has built multiple social graphs:

Now, despite having built all these social graphs over the years, Google wants access to your Facebook Friends, which is an implicit admission of its past social failures.

Microsoft’s Approach To Social: “The Glue”

In a recent blog post, Microsoft described their approach of partnering for social:

Facebook, MySpace, Orkut and QQ have become more general-purpose social networks for all of your acquaintances. LinkedIn, Xing, and Viadeo are great places for professional interactions, …there are great photo and video sharing sites like Flickr and YouTube, and hundreds of others that provide content and let customers post, comment, rate and re-share.

In light of this, we’re not trying to be yet another general-purpose social network, real-time public broadcast channel, or video sharing site. There are great services out there for these things already.

Microsoft’s approach seems to be working. With 330 million active users, Windows Live Messenger is the #4 worldwide app used by active Facebook users, just behind the most popular games like Farmville.

Windows Live is thus connected to 40+ different services, including virtually all of the popular social networks, audio/video/photo/music networks, and anything else you can imagine. They are also partnering with anyone using open standards like OAuth, Portable Contacts, Activity Streams, etc. – no longer a Google USP.

This stealth approach by Microsoft was also identified as Google’s approach by the Altimeter Group last year, but Google has not made much progress since then.

Where Does Google Me Go From Here?

From the latest reports, Google Me is about an additional “social layer” on top of:

  • YouTube – I see this as a primary thrust area for Google (social recommendations)
  • Search – possible enhancements to Social Search
  • Google Maps – greater integration with Latitude, possibly FourSquare?
  • Picasa /Flickr – social sharing enhancements
  • A social gaming platform – from Zynga

The key question is, which social graph will Google use to add this “social layer”? With rivals Facebook and Microsoft partnering closely, Google has one ally left: Twitter. An integration of Google Profiles with Twitter can yield exciting possibilities.

Twitter’s relationship with Windows Live isn’t going too good. This might be Google’s opportunity in disguise. However, it’s going to be an uphill battle.

Update: After writing this post, All Things D reports a deepening of ties between Facebook and Microsoft Bing. This is a direct assault on Google’s bread-n-butter search business. All the more reason why Google needs to reciprocate by deepening its ties with Twitter.

Twitter is increasingly becoming a media company and a pervasive news platform, as Mathew Ingram writes at GigaOm. Why not a Twitter-integrated Google News? A personalized Google News based on users’ social graph on Twitter would be a great start.

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Mapping Startups & Services Filtering For Relevance In A Matrix

After looking at the different approaches to filtering for Relevance, I have been seeking a way to map them visually. There are many different startups competing in this space along with the giants, and a way to map them in a matrix would help us see the big picture of how the battle for relevance is evolving on the social web.

What are the fundamental ways in which these approaches and startups differ? These could form the axis around which we can then proceed to map them.

The Popular – Personalized Axis

Filtering either works by showing us the most popular stuff being shared online, or by understanding our individual preferences and surfacing personalized content. Thus, we have the following axis:

PopularPersonalized

The Serendipity – Search Axis

You either search for content or you see it serendipitously without seeking anything specific. Search is actively initiated by the user and is goal-driven, while serendipitous discovery is gifted with the user being passive at the receiving end. This gives us our second axis:

SerendipitySearch

The Filtering for Relevance Matrix (FORMAT)

We combine these two axes to form the backbone of our visualization. We then place different services within our matrix as per their core filtering approach. The result is the Filtering FOR Relevance Matrix (FORMAT) as seen below:

 

Format

Let us now look at each quadrant closely.

Popular – Search Quadrant

This is the simplest and oldest of all. Search powered by algorithms to surface most popular content online. This also includes other Twitter search services like Topsy. These services are powered by algorithms such as PageRank, PersonRank, Resonance, etc. to surface the most popular result relevant to a query.

This approach dominated the Web 1.0 era before the advent of the social web.

Popular – Serendipity Quadrant

Services in this category help you find the most popular content being shared online across different social networks. These were the next to evolve in the Web 2.0 era, beginning with social bookmarking services like Reddit, StumbleUpon, etc.

There is an element of personalization provided by many of these, in that you “follow” some users, but the motive behind such following is less to seek personalized content, more to seek trending, viral content.

Note how Digg is attempting to move from this quadrant to the personalized quadrant, and facing hurdles along the way.

Search – Personalized Quadrant

A breed of services has evolved around delivering personalized recommendations and content tailored for your needs. Hunch learns about you and acts as a “taste engine”, while Blekko allows you to personalize your searches with slashtags. Google is making forays in this space with its Social Search service, which tries to personalize search results based on your social graph.

Personalized Serendipity Quadrant

This is the hottest space where most of the competition is today.

Twitter Lists are personalized (created by you) and deliver fresh, serendipitous content relevant to your interests. Facebook Likes give you serendipitous discovery from your personal friends. Flipboard provides a social magazine based on your personal social circle on Facebook and Twitter. My6sense delivers new content using ‘Digital Intuition’. Vertical networks like Last.fm deliver music recommendations based on your individual taste. Personalized Twitter newspapers give you fresh content filtered by your social graph on Twitter.

Note how Datasift lies at the center of the matrix. This is because Datasift is a platform providing different filtering services and approaches. Developers may use the platform to develop different services and apps that can lie in any of these quadrants.

How does FORMAT help?

So what is the point of this exercise? Using FORMAT:

  • We see the big picture of how services providing relevance and filtering are evolving.
  • We see how personalized serendipity is the holy grail of the social web right now.
  • We see how different services relate to each other and who is competing with whom and how.
  • We see how identifying the target quadrant is important for any new startup in this space.
  • We see how users provide friction when a service tries to change quadrants (Digg).

If you are involved in a startup aiming to provide filtered, relevant content to users, which quadrant would you target? See how FORMAT helps?

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DataSift Curation Engine Aims for Relevance in Real-time

As I have said many times previously, if 2009 was all about the hype of Real-time, the future is all about capturing Relevance in real-time. Datasift has partnered with Twitter to get the full Twitter firehose and is building a platform to enable curation and filtering in real-time.Datasift

An introductory video about Datasift was posted in their first blog post, which didn’t reveal much about how the platform works. Now, uber-geek Robert Scoble has posted a video of an extensive discussion with Datasift’s founder, Nick Halstead.

Robert Scoble with Datasift founder Nick Halstead

This post is a summary of Datasift as discussed above concluding with my own thoughts.

The Basics

Twitter’s firehose at present has around 800 tweets/sec, or 70 million tweets/day. Datasift can filter this firehose using over 20 variables. Examples of these variables include:

  • Profile information like name, location, bio, number of follows, followers, lists, etc.
  • Text and language of tweets
  • Geo-location of tweets
  • Verified users
  • Source of tweets – web, Seesmic, TweetDeck, etc.
  • Number of Retweets
  • Whether tweet contains a hyperlink

Datasift is a rules-based engine that can filter this firehose using thousands of complex rules and provide a filtered stream in real-time within milliseconds. It is built using a Service Oriented Architecture and has an API.

The Rules

Rules can comprise of any combination of filters using the above variables. Rules can be combined and merged, or added and subtracted, into a single new rule. Stream outputs from Datasift using such rules can become columns in Twitter clients like TweetDeck.

Here are a few examples of how rules can be used:

  • Show me tweets containing “google” from users who don’t have “social media” in their bio, and who have more than 500 followers.
  • Show me tweets from my curated Twitter list of tech brands that have more than 100 Retweets.
  • Show me tweets originating from within a radius of 5 miles from the location of XYZ Conference that don’t have swear words, irrespective of whether their tweets contain the hashtag for the conference.
  • Show me tweets originating from Starbucks shops around the world, of users who are “Verified Accounts”, irrespective of what they’re about.

Datasift’s website is intended as a community website for curators and developers to collaboratively work on developing these rules. You can leverage rules created by others to avoid duplication of effort. Rules are classified with tags, and Datasift provides search, ranking and trending for easier discoverability of rules.

Partnerships for Influence Tracking and Sentiment Analysis

Datasift has partnered with PeerIndex and Klout to enable filtering using their influence and authority scores. It has also partnered with a firm for real-time sentiment analysis.

Thus, any of the above rules can be filtered further using such scores, and a stream of tweets with negative sentiment about a brand or product, combined with any other rules, can be monitored in real-time.

Alerts and Analytics

For esoteric rules that may provide a result infrequently, alerts can be set up. The example discussed is of any politicians from a Twitter list tweeting the word “scandal”. Developers can send these alerts as email, SMS, or notifications on smartphones.

The resulting streams from all rules applied by the engine are stored by Datasift. This data can be extracted, segmented, and analyzed later. For example, this can be used to track the performance of social media campaigns.

Relevance Filtering of Links

Datasift can use TweetMeme and other databases to check the links in tweets, and determine whether they are relevant to a specific topic. Not much details on how this is achieved, but apparently, Nick says that all sites are already classified into different subjects by Tweetmeme and other such databases.

Blekko-style Twitter Search

Datasift has developed a prototype of Twitter search along the lines of Blekko’s slashtags. Thus, along with your query text, you can use filters such as “/nolinks” to get tweets without links, or “/California” to get tweets originating from CA.

RSS Feeds

Compared to the massive volume of the Twitter firehose, the volume of RSS is minimal. Datasift plans to have their own PubSubHubbub server. Developers and third-parties can plugin any RSS feeds and use Datasift’s filtering rules to get an output feed.

Revenue Model

One option is free access to the stream with in-stream ads. Ads will be tailored and designed for the target form factor – desktop/mobile/tablet/etc.

Second option is selling data B2B for developers and brand companies, charged by volume of data consumed.

Prospective Partners

Datasift is seeking to work with startups like Flipboard, who are creating new ways for curated content consumption. This can also include any of the startups focusing on Relevance, such as TwitterTimes or Paperli.

My Thoughts

When I compared approaches to filtering information for relevance, I had suggested that the service most likely to succeed would be the one that supports multiple approaches and platforms. We can easily see that Datasift supports all platforms and several approaches like crowdsourced filtering, influence filtering, location filtering, etc. It is easily the most powerful relevance filtering engine I have seen yet.

The market of end-users for curated real-time content is at present unknown. Startups involved in creating pleasant experiences for consuming content have yet to find a monetization strategy. The degree of Datasift’s success from an end-user perspective is largely dependent on:

  • The creativity of developers and curators to create compelling experiences, and
  • How the monetization strategies of presentation apps fare and how Datasift is able to work with them

Nevertheless, with the amount of content being created online growing exponentially, curation and filtering will eventually become necessities for any social media client. It is just a matter of time.

I also see a bright future on the B2B front. By partnering with influence and authority tracking companies, combined with sentiment analysis, Datasift may already be a compelling choice for brand monitoring and social media reputation tracking.

Lastly, thanks to Robert Scoble and Nick Halstead for the interesting interview.

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Can Blekko be a Disruptor in Search?

Blekko is a new search engine currently in private beta, and I have been playing with it for the past few days. Co-founder Rich Skrenta says upfront that Blekko is not a Google-killer, and I agree. However, for a few search enthusiasts to begin with, it is a very interesting Google alternative to come up in many years.

Blekko Search

If you are unfamiliar with Blekko, read this introductory article by Mike Arrington. For a detailed look, read this in-depth review by Danny Sullivan.

A SlashTag For Techmeme Leaderboard

I wanted to have a handy way to search all the websites that make up the Techmeme Leaderboard. It turned out to be simpler than I thought. A straight import of the OPML file helped create my “/TMTop” slashtag that I could use to get quality search results for anything related to technology.

For generic search terms like “credit card”, the difference between search results from Google and Blekko is obvious:

Google Credit Card

Blekko Credit Card for TMTop

Higher Relevance With Curated Search

When comparing approaches to filtering for relevance, I noted how Google search is built almost entirely on algorithms, with minimal human intervention directly on search results. Being a monopoly in the search business, Google has gone to great lengths to ensure that its search algorithm is fair and impartial with no human bias.

Blekko turns this principle upside-down, by giving end users the ability to curate their search. This mix of human + algorithmic filtering leads to potentially very high relevance of search results. Why potentially?

Keyword vs. Slashtag

Consider an example. Let’s say I’m searching to troubleshoot problems with iTunes on a Windows PC. The key question is: can Blekko’s “iTunes problems /windows” perform better than Google’s “iTunes problems windows”? The answer, at present, is no. Google’s first result is Apple’s official support site for iTunes on Windows, while Blekko doesn’t include www.apple.com as part of its “/windows” slashtag.

In fact, at present, even a plain search for “iTunes problems windows” without any slashtag on Blekko doesn’t return the Apple support site in the first few results.

These are difficult challenges for Blekko. Slashtags may not be as effective as you might think. This is because curation is an either-or affair – there is no ‘maybe’ as there can be deep inside an algorithm.

Combining Social Features with Search

Blekko has added social features by enabling you to “follow” other users’ slashtags. This means those who can aggregate a carefully curated set of websites within a slashtag stand a chance of being followed by several other users. This sounds appealing as anything social does these days.

But a reality check: who makes “following” popular on the web? Celebrities and Websites/Blogs whose primary objective is driving traffic to their own content. A slashtag may be a curator’s achievement, but it drives traffic to various sites by definition. Thus, I don’t see any popular brands, celebrities, or content creators to drive the social features of Blekko, hence I suspect it will remain restricted to the minority of search enthusiasts.

Impact on SEO: Slashtag Optimization (STO)?

Will Blekko’s human curation mean that algorithm-focused SEO will suffer? That largely depends on market share of Blekko’s adoption. Greg Sterling has a nice post discussing this issue.

Imagine being able to set default slashtags in your search preferences that filter content farms, adult websites, etc. Search will get a boost in effectiveness of several orders of magnitude. This, coupled with the transparency Blekko brings to the table about its internal SEO metrics, is one of the best things to happen in search, in my opinion.

Even if a minority of search enthusiasts adopt Blekko, I see two possibilities:

  • Google may tweak its algorithm to penalize content farms, as is being suspected
  • Google may offer tools to filter the web in its own searches

In my opinion, if either of these happen, Blekko has proved to be disruptive.

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5 Suggestions for Twitter’s Whom To Follow

Here are 5 suggestions for Twitter’s “Who To Follow” feature, that I have seen being mentioned in the Twitterverse:

  1. Avoid users who have set tweets as Private
  2. Avoid users who haven’t tweeted for past 15 days or have less than 10 tweets overall
  3. Avoid users I have added to Lists
  4. Avoid famous celebrities everyone knows
  5. Avoid users I have followed and unfollowed before

Twitter Who To Follow

These simple things will improve the effectiveness of Twitter’s suggestions greatly.

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Another day and I read another post on how Facebook’s Like button is slowly obliterating Google’s Link as the next currency of the web. The pondered question in this case is what is going to be Google’s counter-offensive against the Like.

The assumption is that Google as a search engine has worked on the principle of ranking web pages according to the number of other pages linking to it. Well, here’s the deal: when a person likes something on the web, in most cases, a link is created. Google can see this Link, and hence can understand and incorporate the Like, in its scheme of things.

This mechanism has already been publicized by Google, but I’m surprised how many folks still keep discovering it as if it were something new. For example, see this from yesterday.

Google’s Invisible Like Mechanism

Google’s Like mechanism was announced by Google in Oct 2009 in a blog post announcing Social Search, which linked to this help article that explains how it works in the background.

Google Socal Search Like Button

The battle is between Facebook’s Like and Google’s Profiles. For Facebook to capture your Like, it requires you to have an account on Facebook. For Google to capture your Likes, you need to have a Google Profile. Now, let’s compare what Facebook and Google can capture:

Facebook can capture only your Facebook Likes.

Google Profiles can capture:

  • Public content you share on Facebook
  • All tweets on Twitter
  • All shares on Google Reader
  • All shares on FriendFeed
  • All status updates on LinkedIn
  • All favorites from YouTube
  • All likes, faves, photos from Flickr and Picasa
  • All bookmarks from Delicious
  • All stories you have Digged
  • Everything you have Stumbled Upon
  • Everything you have Disqused
  • All your Blogger and WordPress blog posts
  • And dozens of existing and future sites using the XFN or FOAF standards (see FAQ)

Get the picture? From a technical standpoint, Google has all the arms and ammunition to capture Likes across a plethora of social websites. If you have a Google Profile, every action on any of your connected social websites (sort of) results in a Like being submitted to Google.

Google’s Challenge

Presentation: Currently, Google is surfacing all this behind-the-scenes information only through Social Search results. Google doesn’t have a social web site where you can see your friends’ Likes and interact with them. This is potentially the core of what Google Me is all about.

Numbers: Facebook has 500 million, very few have Google Profiles. We have been waiting for that big push for Google Profiles. It is imminent, and apparently, very close.

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