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?

Tagged with:
 

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.

Tagged with:
 

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.

Tagged with:
 

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.

Tagged with:
 

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.

Tagged with:
 

Schmidt: Google Buzz is “an extension of Gmail”

Some interesting comments by Eric Schmidt during the Techonomy conference.

From CNET:

Schmidt said that Buzz, by contrast is doing well with tens of millions of users, basically Gmail users that also use the short-status product.

"Today Buzz is really an extension of Gmail," he said.

From TechCrunch:

“The Buzz team is doing very well,” Schmidt said. But he noted that “we tend to lump Buzz into the Gmail success.

Compared with the initial hype, I felt these came across as pretty disparaging remarks about Buzz. I am afraid of seeing it languishing as “just an extension” of Gmail.

To my mind, this also indicates the following:

  • Google Buzz will never be separated from Gmail, what some considered the key to unlocking its true potential
  • Google Buzz never was and never will be a competitor to Facebook
  • Google Me is indeed just a social gaming network, as the WSJ had reported
  • Google Me will not be integrated with Google Buzz
  • Since Buzz is tightly integrated with Google Profiles, Google Me isn’t likely to be
  • “Tens of millions” typically means 20-30 million. Thus about 15 to 20% of the estimated 173+ million Gmail users use Google Buzz.

This clarifies a lot of ambiguity over Buzz and Google Me. But that’s reading a lot into Schmidt’s comments, so we still have to wait and see.

Tagged with:
 

Challenges for PeerIndex, Lessons for Klout

Continuing the discussion in my earlier post, PeerIndex: Rating Authority and Relevancy, let’s look in detail at some of the challenges for PeerIndex and what lessons Klout can learn.

Challenges for PeerIndex

1. Get More Accurate Scores of the Big Guys

I saw several folks yesterday seeing their PeerIndex score as Zero. Apart from that, look at just these examples:

Scoble PeerIndex

JayRosen PeerIndex

Something is clearly not right. These are two of the most influential people in tech and media. These are folks who can make or break a startup, and you better get their scores right if you’re to gain any credibility and leverage their influence.

2. Adapt to Different Content Curation Approaches

Different people use Twitter in different ways. For example, Louis Gray’s sharing is primarily through Google Reader, which is tweeted by @lgstream. Robert Scoble’s content curation is through his Twitter Favorites.

These are influential early-adopters, who consume and filter from a massive information stream, and have hence tweaked their Twitter usage habits to suit their needs. The use of their primary Twitter account is for conversation, while curated content gets a separate, dedicated feed.

PeerIndex probably needs to find a way to incorporate multiple Twitter accounts and Twitter favorites into its ranking.

3. Offline Influence Tracking

This is a tough nut to crack and I’m only reiterating it here for the sake of completeness.

Lessons for Klout

1. Diversify Beyond Twitter

If you’re not leveraging Facebook, you’re yet to capitalize on the social web. The Twitterverse is a significant, but small part of the social web.

2. Remember Your Promises

In January 2010, Klout announced that they will be releasing lists of the top influencers for a new country every week. By August 2010, how many country lists have been published? Three – Brazil, UK, and Germany.

3. Leverage Twitter Lists

For a startup aiming to build definitive influence ranking on Twitter, you would think you can readily follow top influencers by region, topic, etc. from their Twitter account. Here are the only lists Klout has created on Twitter:

Klout Lists

This is a failure to capitalize on and leverage a core Twitter feature.

4. Don’t Sacrifice Functionality For UI

I have said it before and I’ll say it again: If You’re Removing Features, Please Tell Your Users!

In May, Klout launched a revamped site with a new classification system and UI. What was not announced was that you no longer had the ability to view the top influencers in a topic, or see the Klout scores of users in a Twitter List.

It is critical for users to be able to use Klout not just to check scores of people they know, but to aid discoverability, easily create Twitter lists using Klout and so on.

Both these startups are very innovative and doing some great work. These are my thoughts on some of the challenges they face and lessons they can learn.

Tagged with:
 

PeerIndex: Rating Authority and Relevancy

Since Analyzing Twitter Lists-Follower Ratio As An Indicator Of Influence, I have been occasionally checking out Klout as the least followers-driven and interesting influence tracker on Twitter. Now, there’s a new kid in town in the influence measurement space and more – PeerIndex.PeerIndex Logo

Coverage of PeerIndex on Guardian, VentureBeat, and TechCrunch Europe focused only on the influence measurement aspect. Azeem Azhar, founder of PeerIndex, is a former Reuters Innovation Head, who’s also worked with The Economist, Guardian, and BBC. This rich media background drives the topic-based approach of PeerIndex and distinguishes its vision from Klout.

Comparing Ranking Methodology with Klout

Klout Score is a normalized ranking based on:

  • True Reach: The people who regularly pay attention to what you say.
  • Amplification Probability: How far (and often) your content spreads.
  • Network Influence: The influence of your engaged network.

PeerIndex Score has a fundamentally different approach. Rather than calculating a global score first, it defines topics and then calculates an Authority Score in that topic. The rationale behind this is sound: that experts in one topic are not necessarily experts in another.

The PeerIndex Score is a normalized ranking based on:

  • Authority: Quality of the links you share and content you recommend.
  • Activity: How active you are in a topic based on relevance.
  • Audience: Number of people you can reach after discounting spam/gamed/inactive accounts

The key difference between the two approaches is:

PeerIndex also analyzes quality of content you share, rather than just monitoring Twitter activity.

MyPeerIndex

Influence Tracking vs. Relevancy Rating

While Klout is focused solely on Twitter mechanics, PeerIndex also focuses on relevancy of content shared in the context of a topic. Azeem tells me that they are presently tracking over 100 topics, and more topics will be made public in phases.

LinkedIn and Facebook Integration

PeerIndex integrates your LinkedIn and Facebook profiles in scoring, while Klout only works with Twitter. I think this is a huge difference that will affect the usefulness of such services. If you have a Facebook fan page with lots of fans, and are connected to other influencers on LinkedIn, Klout won’t take that into account, but PeerIndex can.

At present, it only considers raw number of connections, but may use more engagement metrics from these services in the future.

Blog Integration

Another interesting factor is the ability to add your blog or website to your profile. At present, there is little effect of adding a blog on one’s PeerIndex score, but it is a step in the right direction. It will be interesting if PeerIndex can assess the authority of your blog and factor it in its ranking.

PeerIndex Add Blog

People Focus: No Brands

The team has made a conscious decision to keep brands and organizational accounts out of its site. The focus is on finding people, exclusively. So, @TechCrunch and @Mashable may have high Klout scores, but they don’t have a PeerIndex.

[Update: As Azeem clarifies in the comments, brand scores are kept internal to the system, just not made public.]

Valuing Curation: Oversharers Penalized

I had written in March about oversharing in social media and how curation increases your reputation. Now, PeerIndex puts this principle in action: there is a cost for oversharing. Noise in your feed reduces the relevance of your shares, hence your ranking goes down.

Challenges: Real World Influence

As with any web service, the challenges for PeerIndex are that there is no standard way all influencers use the social web. For example, authors of real-world books, who may in fact be really influential, may not be active users of the social web. Some may not use Twitter at all. In the end, these services are really useful only for people discovery on the social web.

Future: Authoritative News Aggregation

PeerIndex plans to sell a premium service to brand marketing and PR, to help them identify which influencers their clients should target. More interestingly, Azeem also shared with me the idea of collating the opinions of different authorities to create an aggregated newspaper.

The possibilities are fascinating. Imagine sections from Flipboard – like FlipFinance or FlipTech being powered by topic-based authorities from PeerIndex. According to Azeem, their topic model is not constrained and can be extended to any number of topics. What we have here is an Open-ended Authority-cum-Relevance Ranking Engine.

API? Coming soon.

Azeem’s Interview With Scoble

Do check out this really insightful interview by Robert Scoble with Azeem Azhar.

Tagged with:
 

What We Really Need: Discovering Whom To UnFollow

Twitter is rolling out a new feature to help you discover new people to follow:

The algorithms in this feature, built by our user relevance team, suggest people you don’t currently follow that you may find interesting. The suggestions are based on several factors, including people you follow and the people they follow.

This is a very welcome move by Twitter. TechCrunch says they’re building a Social Graph, while VentureBeat suggests a PeopleRank algorithm powering these suggestions.

The problem? Twitter badly needs a Matt Cutts.

Active Users

Here are stats on number of tweets by Twitter Users by RJMetrics from Jan 2010:

updatedistribution

  • 80% of all Twitter users have tweeted fewer than 10 times.

That means only 20% are active users.

The 2009 Annual Report from Barracuda Labs independently confirms these findings.

  • 34% of Twitter users have no tweets
  • 73% of users have less than 10 tweets

Spam Accounts

Now, from the remaining 20% of “active Twitter users”, how many users are spam?

According to TwitSweeper in March 2010: 5%.

These are accounts who tweet "make money fast online!", "multiple sources of passive income", "view my naked pics!", etc.

That leaves 15% of Twitter users who are real and may be considered worth following.

Why This Is A Problem

If Twitter is trying to build a meaningful, relevant social graph, they have to clean up first.

Twitter’s PeopleRank faces the same challenge as Google’s PageRank: Blackhat SEO. These spam accounts are followed by each other and by other fake accounts – all to provide a semblance of a active social user graph and avoid algorithmic detection. These are virtually indistinguishable from real users and will become part of the suggested users ecosystem.

How many times do we encounter spam accounts on Facebook? How many times do we see spam results in the first page of Google search results? In contrast, how many times do we get @replies from spammers on Twitter?

A contaminated social graph or PeopleRank system is harmful to Twitter from an investor, user, and advertiser point of view. It will be great if Twitter is able to suggest whom to unfollow and get rid of all these inactive, fake, and spam accounts.

Tagged with:
 

Why We’re Moving From Status Updates to Q&A

Within the past few weeks, Quora went public in June, Ask.com reverted to its Q&A roots, and Facebook Questions were formally introduced.ask-logo

Why this sudden trend and momentum towards Questions and Answers?

First, Status Updates are passé. The social web needs to move beyond.

Second, Check-ins were an evolution of the status update, but they lack mainstream appeal, have privacy related issues, and are limited in scope (you have to move to check-in to a different location).

Third, Q&As are more attractive to both users and businesses:

  • Q&As vastly increase the actual usefulness of a social site by several orders of magnitude. This is the obvious, perceived benefit for users.
  • Meaningful questions reveal more about a person than mindless status updates. This leads to better profile information than what people may or may not reveal in their profiles.
  • Questions reveal Intent. Advertisers are more likely to target “Which are the best places to travel to Goa?” (Question) than “Wish I could spend the New Year in Goa!” (Status Update).
  • Questions have a much greater possibility of eliciting responses, leading to greater interaction, translated as more time spent using the site/service.
  • Answers reveal more information about a person’s expertise and interest than what people may or may not reveal in their profile.

Update: To illustrate the last point further, let’s say you answer the question “What’s the best telescope to buy at home?” and it gets voted up. Bingo! Now, even if you don’t have Astronomy listed as a hobby or interest, Facebook knows you’re an Astronomy enthusiast. Also remember, all this information is public and search engines would be glad to get their hands on it. Imagine Blekko with a slashtag search of all Q&A sites – it would be a gold mine for marketers.

What’s next? I wouldn’t be surprised if check-ins and Q&A were tied together. Answers from a person geographically closer may be of higher relevance for certain type of questions. You can add special Badges for the most answered questions about a location, and you get the next version of “Mayor” in FourSquare.

Tagged with:
 

Switch to our mobile site