social search

Topsy lets you search tweets from 2006, look up old cringeworthy posts

Source: http://www.engadget.com/2013/09/05/topsy-expanded-twitter-search/

DNP Topsy now with tweets since 2006

Next time you’re feeling nostalgic and want to peruse old Twitter posts — such as in 2006, when Pluto was demoted to dwarf planet status — you might want to pay Topsy a visit. The social search engine, which could previously look for posts up until 2010, has expanded its archives to include tweets from as far back as Twitter’s birth in 2006. Simply input terms in the search box, and you’ll find their newest and oldest mentions on the site. Even better than that, you can use the site to read every single tweet a user has ever posted by querying “from:yourusername,” making it easy to look for the first time you tweeted about Lady Gaga’s wardrobe. Before you run off and facepalm at your old tweets, though, check out @engadget’s first one by Ryan Block after the cut.

 

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Thursday, September 5th, 2013 news No Comments

Hey Pal, You Screwed Yourself (GOOG)

Source: http://www.businessinsider.com/google-to-twitter-hey-pal-you-screwed-yourself-2012-1


Guy from ChicagoYesterday, Google announced a new, optional feature.

Users who turn the feature on will now see personalized search results that link to content from social networks.

Google called it “Search plus Your World.”

After the launch, Twitter complained, saying that Search plus Your World did not include content from Twitter.

Google responded:

We are a bit surprised by Twitter’s comments about Search plus Your World, because they chose not to renew their agreement with us last summer (http://goo.gl/chKwi), and since then we have observed their rel=nofollow instructions.

Translated into normal person English, Google is saying, “Hey Twitter, the only reason we didn’t include your content in Search plus Your World is because you asked us not to.”

Here’s what we think is going on: Google used to pay Twitter for “firehose” access to all the content on Twitter. It sounds like this summer, Google told Twitter that it would no longer like to pay for that access. Twitter – it seems – said OK, you can’t have access to that content anymore.

So who’s right and who’s wrong?

From Twitter’s perspective, you could argue that Google is trying to shake it down, telling Twitter: Give us your content for free or we’ll point all our users at Google+ instead of Twitter!

From Google’s perspective, you could argue that Twitter is trying to charge Google for access to content and complaining when Google said no thanks and made do without. If getting into “Search plus Your World” is so important to Twitter, maybe it shouldn’t charge Google for getting access to Twitter.

Danny Sullivan and MG Siegler are doing a great job covering this story.

Please follow SAI on Twitter and Facebook.

Join the conversation about this story »

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drag2share – drag and drop RSS news items on your email contacts to share (click SEE DEMO)

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Wednesday, January 11th, 2012 news No Comments

Aardvark Publishes A Research Paper Offering Unprecedented Insights Into Social Search

Source: http://feedproxy.google.com/~r/Techcrunch/~3/IMDRrISRf-8/

In 1998, Larry Page and Sergey Brin published a paper[PDF] titled Anatomy of a Large-Scale Hypertextual Search Engine, in which they outlined the core technology behind Google and the theory behind PageRank. Now, twelve years after that paper was published, the team behind social search engine Aardvark has drafted its own research paper that looks at the social side of search. Dubbed Anatomy of a Large-Scale Social Search Engine, the paper has just been accepted to WWW2010, the same conference where the classic Google paper was published.

Aardvark will be posting the paper in its entirety on its official blog at 9 AM PST, and they gave us the chance to take a sneak peek at it. It’s an interesting read to say the least, outlining some of the fundamental principles that could turn Aardvark and other social search engines into powerful complements to Google and its ilk. The paper likens Aardvark to a ‘Village’ search model, where answers come from the people in your social network; Google is part of ‘Library’ search, where the answers lie in already-written texts. The paper is well worth reading in its entirety (and most of it is pretty accessible), but here are some key points:

  • On traditional search engines like Google, the ‘long-tail’ of information can be acquired with the use of very thorough crawlers. With Aardvark, a breadth of knowledge is totally reliant on how many knowledgeable users are on the service. This leads Aardvark to conclude that “the strategy for increasing the knowledge base of Aardvark crucially involves creating a good experience for users so that they remain active and are inclined to invite their friends”. This will likely be one of Aardvark’s greatest challenges.
  • Beyond asking you about the topics you’re most familiar with, Aardvark will actually look at your past blog posts, existing online profiles, and tweets to identify what topics you know about.
  • If you seem to know about a topic and your friends do too, the system assumes you’re more knowledgeable than if you were the only one in a group of friends to know about that topic.
  • Aardvark concludes that while the amount of trust users place in information on engines like Google is related to a source website’s authority, the amount they trust a source on Aardvark is based on intimacy, and how they’re connected to the person giving them information
  • Some parts of the search process are actually easier for Aardvark’s technology than they are for traditional search engines. On Google, when you type in a query, the engine has to pair you up with exact websites that hold the answer to your query. On Aardvark, it only has to pair you with a person who knows about the topic — it doesn’t have to worry about actually finding the answer, and can be more flexible with how the query is worded.
  • As of October 2009, Aardvark had 90,361 users, of whom 55.9% had created content (asked or answered a question). The site’s average query volume was 3,167.2 questions per day, with the median active user asking 3.1 questions per month. Interestingly, mobile users are more active than desktop users. The Aardvark team attributes this to users wanting quick, short answers on their phones without having to dig for anything. They also think people are more used to using more natural language patterns on their phones.
  • The average query length was 18.6 words (median of 13) versus 2.2-2.9 words on a standard search engine.  Some of this difference comes from the more natural language people use (with words like “a”, “the”, and “if”).  It’s also because people tend to add more context to their queries, with the knowledge that it will be read by a human and will likely lead to a better answer.
  • 98.1% of questions asked on Aardvark were unique, compared with between 57 and 63% on traditional search engines.
  • 87.7% of questions submitted were answered, and nearly 60% of them were answered within 10 minutes.  The median answering time was 6 minutes and 37 seconds, with the average question receiving two answers.  70.4% of answers were deemed to be ‘good’, with 14.1% as ‘OK’ and 15.5% were rated as bad.
  • 86.7% of Aardvark users had been asked by Aardvark to answer a question, of whom 70% actually looked at the question and 38% could answer.  50% of all members had answered a question (including 75% of all users who had ever actually interacted with the site), though 20% of users accounted for 85% of answers.
Information provided by CrunchBase


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Tuesday, February 2nd, 2010 digital No Comments

What is Web 3.0? Characteristics of Web 3.0

2009 06 16 What Is Web 3.0

2009 06 16 What Is Web 3.0 – Presentation Transcript

  1. What is Web 3.0? Dr. Augustine Fou June 16, 2009. June 16, 2009.
  2. Evolution of the Internet microprocessor 40 yrs 10 yrs 20 yrs 5 yrs present web internet 2.5 yrs social networks e-commerce 1.5 yrs Web 1.0 Web 2.0 Web 3.0? June 16, 2009.
  3. Evolution of the “Web” content commerce search social networks social content social search social commerce As each stage reaches critical mass, the next stage is tipped into present June 16, 2009.
  4. Key Characteristics present web 1.0 web 2.0 web 3.0
    • Speedy
    • more timely information and more efficient tools to find information
    • Collaborative
    • actions of users amass, police, and prioritize content
    • Trust-worthy
    • users establish trust networks and hone trust radars
    • Content
    • content destination sites and personal portals
    • Search
    • critical mass of content drives need for search engines
    • Commerce
    • commerce goes mainstream; digital goods rise
    • Ubiquitous
    • available at any time, anywhere, through any channel or device
    • Individualized
    • filtered and shared by friends or trust networks
    • Efficient
    • relevant and contextual information findable instantly

June 16, 2009.

  1. Illustrative Examples – retail/shopping present web 1.0 web 2.0 web 3.0
    • what friends bought or want to buy
    • drag-to-share items which friends know friends are looking for
    • item collections
    • value in the aggregation

overstock.com amazon.com FB app: MyFaveThings

    • contextual reviews
    • reviews of reviews
    • what others bought
    • individualized recommendations

June 16, 2009.

  1. Illustrative Examples – social networks present web 1.0 web 2.0 web 3.0
    • aggregates all your online identities
    • syndicates all your updates to all social networks
    • social actions visible to friends
    • trust networks across geography, time, and interests
    • collection of personal homepages

geocities.com facebook.com peoplebrowsr.com June 16, 2009.

  1. Illustrative Examples – restaurant reviews present web 1.0 web 2.0 web 3.0
    • Yelp content vetted through a user’s trust network and individual recommendations made based on situation and need, in real-time
    • user submitted reviews
    • related items based on similarity of user preferences
    • infrequent publication
    • centralized editorial control

zagat‘s yelp need reco for great Italian + GPS + Yelp 5-star Babbo, been there, love it June 16, 2009.

  1. Illustrative Examples – photos present web 1.0 web 2.0 web 3.0
    • real-time, contextual “do you like this knit shirt?”
    • friends give immediate feedback
    • share photos with friends and strangers
    • enable visitors to tag and comment
    • individual albums

kodakgallery.com flickr.com ? June 16, 2009.

  1. Illustrative Examples – real estate present web 1.0 web 2.0 web 3.0
    • information vetted by fellow users, recommended directly an in context
    • listings plus relevant information like school zones, comparable sales, alerts
    • listings based on parameters

corcoran.com streeteasy.com trulia iphone app June 16, 2009.

  1. Illustrative Examples – encyclopedia present web 1.0 web 2.0 web 3.0
    • content is ubiquitous and available through any channel or device
    • trust network proactively forwards relevant info to user who needs it
    • created, updated, and edited (policed) by user actions
    • digitized version of printed encyclopedia

britannica.com wikipedia.com chacha.com June 16, 2009.

  1. Illustrative Examples – online coupons present web 1.0 web 2.0 web 3.0
    • coupons delivered contextually and proactively when user needs it (without the user even asking for it)
    • instant feedback
    • community action makes it more accurate and useful for others
    • collection of online coupons – value in the aggregation

dealcatcher.com retailmenot.com June 16, 2009.

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Dr. Augustine Fou is Digital Consigliere to marketing executives, advising them on digital strategy and Unified Marketing(tm). Dr Fou has over 17 years of in-the-trenches, hands-on experience, which enables him to provide objective, in-depth assessments of their current marketing programs and recommendations for improving business impact and ROI using digital insights.

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