Fashionably late*, Google Search’s

Simply click on the checkbox next to SearchWiki and you will “Hide the ability to share, promote, remove, comment, or add your own results”

All good now :)

* Friendly reminder: Marissa Mayer promised that Google Search Wiki would soon have a toggle button that allow people to turn it off (“early Q1.”/2009)

They have ran a small survey using Amazon Mechanical Turk (which is a pretty cool concept for itself) asking ~150 users to describe their search experience.

IMHO, their data analysis resulted in a very intriguing social search model.

“As we outlined through the model, social inputs may help users throughout the search process. Before searching, social interactions may help establish the requirements for the actual search task. During search, especially for self-motivated informational searches, users may talk to others for advice, feedback, and brainstorming to improve their search schema and query keyword selections. After search, users may still wish to engage with others to collect additional feedback or to share knowledge gained during the search.”
(Brynn M. Evans and Ed H. Chi – Towards a Model of Understanding Social Search | 2008)

P.S. It seems like we are on the right direction… :)

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Quick (a.k.a. too late) Y.A.S.B.P. (a.k.a. Yet Another Shameless Blog Plagiarism) update: I had a strange (yet familiar) feeling that I have read about this research somewhere before but I didn’t remember where so after I submitted this post, I have found that Ofer had picked it up way before me and as always, wrote a much better post about it…

——— Blitz Introduction to The Social Graph ———

The social graph is just a simplified mathematic abstraction when nodes are people and edges are relations between them.

In the last decade the internet have became more social than was ever expected it to be with the rapid growth and adaptation of social networks, social media and user-generated contributions and interactions. 

Nowadays, there is a growing feeling that it is feasible to model and map the social web into a real-life social graph replication.

——— Pragmatic Overview on The Social Graph Challenge ———

Modeling | Building | Processing | Size | Architecture

(1) Modeling the Social Graph

*** Vocabulary 

To better understand how complicated it is to create a vocabulary for expressing metadata about people, their interests, relationships and activities you should simply pay a quick visit to the FOAF Project technical specification page

The FOAF (“Friend of a Friend”) Project  has the most comprehensive model available today and it is still lacking some basic modeling granularity e.g. time awareness metadata, no privacy model, poor relationship model 

*** The Social Cloud

It is common mistake to forget that people are more than just flat internet identities (e.g. Linked profile) and to complete the profile modeling we must add all their content to the graph e.g. Personal Blog, Flickr images, YouTube Videos, Delicious bookmarks, Tweets, Blog Comments etc.

Modeling all these content and consumption types will yield a broader definition (a.k.a. The Social Cloud) with even more complex modeling challenges.

(2) Building the Social Graph

*** The Paradigm Shift

While conventional internet crawlers, follow hyperlinks within web pages and treat pages as plain-text, social crawlers should have social-”awareness”:

• Identify and extract people identities fragments (e.g. social network profiles, blog authors)
• Identify relationships (e.g. social networks connections, blog-roll fans)
• Identify relations between content and people (author, bookmark, reference etc.)

*** The Standards Dilemma – No Silver Bullet

Beside FOAF, there are several open standard like RSS, ATOM for content syndication and microformats like HCard, XFN for profiles and network discovery,  that seems promising and can help with the identification quest but although this is being pushed by giants (e.g. Google Social Graph API) the adaptation is still low and have many correctness and corruptions issues - e.g. all these people claimed to be WordPress.com using the XFN (rel=”me”) microformat 

*** The Promise of Structured Sources (a.k.a. The structure myth)

The Myth: Most social Media sites (e.g. FaceBook, LinkedIn, MySpace, Flickr etc.) have a public available structured profile pages so in principle all need to be done is some XPath magic on HTML DOM to finish the parsing task.

But… Most of the work isn’t parsing but data modeling which require deep understanding of each site user model and usage

• Many Social Media sites have EULA restrictions which prohibit any access or use to the site content but if you are lucky you will get some offical API’s instead.
• Social Media sites have many (~weekly) structural changes in their CSS/HTML.
• Social Media sites have many changes (~monthly) in their data privacy policy and have complex privacy model which create inconsistency in profile, network and content presentation.

*** Few more Challenges with Social Crawling:

• Privacy-Ownership-Control - The data is the property of the users
• Unstructured Sources – It isn’t a trivial task to extract social entities from unstructured sources (e.g. blogs) and might require offline semantic processing on your collected data.
• Cross Network Relations – How to find those important hidden cross network relations e.g. between the biggest reliable network graph (e.g. FaceBook) and the richest content contributions (e.g. Blogosphere, YouTube, Flickr etc.)
• Identify Social Signs (e.g. Social Widgets, Comments, Blogroll etc.)
• Social Graph Update Mechanism and crawlers distribution
• Profiles Canonization 
• …

(3) Processing the Social Graph

*** The Identity Crisis

• Filtering Impersonation e.g. all these site use XFN (rel=”me”) to “say” they are TechCrunch
• Identify and have different modeling for non-individual identities (groups, shared authorship) e.g. Knitters Blog with 629 knitting contributors :)
• Strive to merge identities  (a.k.a. profile fusion) when possible e.g. Moti Karmona in LinkedIn and Moti Karmona in FaceBook could be two instances (/profiles) of the same person and merging this profiles will enable:
  • Cross network connectedness => Bridging between network richness (e.g. FaceBook) to content richness (e.g. Blogosphere)
  • Richer people representation using identities aggregation => Richer networks
• The Fusion Challenge: You can pay a short visit to the nearest social aggregator directory but you can’t get away from some more complex algorithms for disambiguating web appearances of people with more common names like James Smith who doesn’t “play” in the social aggregation playground (like 98.7% of the graph).

*** Graph Enrichment 

• Implicit Relations - Enrich the network with “implicit” relationships (Colleagues, Graduates, Neighbors) e.g. I have a LinkedIn profile and all my connections are hidden for public crawlers but the fact I work in Delver  is public so if Delver is startup company with less than ~50 people than there is a good chance I know all the other workers in Delver => This simple heuristic rule can create an implicit relation between me and other workers of Delver without me explicitly claim that I know them (as I did in FaceBook)
• Generating the inverted relations when needed Followed vs. Follower
• Deeper, semantic extraction of social entities un-structured content

(4) The Social Graph Size

Let’s have some quick (and very dirty) guesstimates:

World Population is approx. ~6.7 Billion / 22% Internet penetration => 1.5 Billion internet users 

Let’s say 65% of these users have some kind of presence in Social Media (~20% have more than one) => ~1 Billion Profiles x ~10 content items per profile

+ 1 Billion Profiles Nodes x ~100 network relations per profile  => ~110 Billion Graph Edges + ~10 Billion Graph Nodes

It is highly depended on graph implementation but with this numbers, you can easily find yourself with ~1-2 Terabytes of graph metadata alone (without contents and profiles*) 

(5) Two Cents on Social Graph Architecture

Updating and querying gigantic, dynamic, distributed, directed, cyclic, colored, weighted graph have “some” algorithmic, computational complexity – a little more complex than a blog post could cover…;-)

You can take a quick look at the tiny 15 Giga, 25 million nodes graph implementation in LinkedIn to get a glimpse to the technological challenge … 

* Note: Indexing content and profiles data (e.g. for Building a Social Search Engine) is an architecture challenge equivalent to any modern search engine with ~10 Billion documents index

This is only the tip of the iceberg but it is more than enough for one blog post ;)

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Credit: All the images were taken from Tamar Hak‘s amazing artwork – creating The Delver Kid image.

Google, Yahoo, Ask and Lycos have released* their top search terms for the past year (2008) and I have aggregated it to your convenience in one happy table below.

I don’t have anything smart to say about it but I did manage to pull out five intriguing  insights.

My Five Cents:

• As done last year, it seems like Y!  have removed all the  navigational queries from their report (I wonder why ;)

• “Poker” is the “Top Search Term Of The Year”  for for the 3rd consecutive year on Lycos… (what is Lycos? :)

• Though she didn’t make it to the White House, US vice-presidential candidate Sarah Palin captured the zeitgeist of internet users in 2008 while Obama in the 6th place.

• IMHO, Ask.com is just being too honest in their report – 50% of Ask search terms are navigational queries and the rest are boring.

• Britney Spears has been the most popular search term at Yahoo for seven of the past eight years! 

Top Search Terms | 2008

Update (18 Dec. 2008): Top 10 search queries that people used on Delicious in 2008 are: news, blogs, reference, wiki, restaurants, hotels, css, web 2.0, artists, music… I think it is loud-and-clear that the biggest bookmarking site isn’t fulfilling its search potential (!)

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* Note:  Microsot (Live) didn’t released the updated list until now and AOL didn’t break out overall terms so wasn’t included here.

With Google SearchWiki, signed-in Google users can now customize their search experience by re-ranking, deleting, adding and commenting on search results. 

So…

• The re-ranking changes you make are private and only affect your own searches. 
• Your comments are visible to the public 

Random thoughts on Google Search Wiki:

• You need to be very brave to change usability patterns in your world-leading-search-cash-cow (!)
• Why wasn’t it tested as yet another interesting Google Lab project?
• The arrows “soup” is really too much for the lonely-searcher –> way too many arrows if all you wanted is just search.
• The comments I saw until now are mainly spam or not interesting.
• The most important feature in Search Wiki is a way to turn it off but it is still missing…
• It is a good time to change your default search engine ;)
• Is it only me or Search Wiki have the lively smell all over it?

I must be missing something since the Google guys are very far from being stupid (to say the least) and it will be a very interesting to see if Google will change the search experience yet again with this move.

Update (10 Dec. 2008) : Marissa Mayer promised that Google Search Wiki would soon have a toggle button that allow people to turn it off (“early Q1.”) – I can’t wait… :)

This is a cool set of simple APIs that can give you access to everything you ever wanted in Y! but was afraid to ask for…

Yahoo! Social Platform (YSP)
// The Yahoo Social Platform is a set of RESTful APIs for Profiles, Connections, Updates, Contacts and Status.

Yahoo! Query Language (YQL)
// The Yahoo Query Language is a web service that functions much like SQL (see example below)

OAuth Authentication
// OAuth is the authentication and authorization standard Yahoo has decided to use when giving third parties access to Yahoo user data.

Yahoo! Applications Platform (YAP)
// Currently very limited and in a restricted sandbox.

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Example: How to use YQL APIs to access MyBlogLog profiles?

Simply ask for all the community members of MyBlogLog community with this YQL:

select * from mybloglog.members.find where community_id in (select id from mybloglog.community.find where name=”Karmona Pragmatic Blog”)

And once you have the IDs you can ask for my personal profile by:

select * from mybloglog.member where member_id =”2008070609482910″

Well… together with the existing BOSS API, this set of APIs is a powerful enablers to the Y! development network and I am sure some cool stuff are going to emerge from this innovative move…

Amazing!!!

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* You can have more YQL experiments using the YQL Console

** Boss Hack Day is coming to Tel-Aviv | November 6, 2008 @ Feature (!!!)