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AT&T Takes a Commanding Lead… In Dropped Calls
Source: http://gizmodo.com/5535410/att-takes-a-commanding-lead-in-dropped-calls
AT&T may have improved their dropped call performance lately, but they’ve still got a lot of work to do. According to a recent survey from ChangeWave Research, AT&T customers experience 3x the percentage of dropped calls as Verizon users. Ouch.
Maybe the least surprising part of the survey was that those dropped calls led to consumer dissatisfaction, though this time AT&T shared the dishonorable mention with T-Mobile:
There’s no question that AT&T has put serious resources behind improving their network, and major improvements are expected for this summer. But until users start actually feeling those improvements in their day to day lives—without having to resort to a MicroCell 3G—there are going to be a lot of folks interested in jumping ship if and when the iPhone ever hits Verizon. [ChangeWave via PC World]
Android Phones Surpass iPhone in Web Traffic
Source: http://lifehacker.com/5525578/android-phones-surpass-iphone-in-web-traffic
According to data collected by mobile advertising network AdMob, Android phones have surpassed the iPhone in mobile traffic—at least in terms of ads served to the devices, which is a pretty good measure for overall traffic. As mobile browsers account for more and more of our online time, it’ll be interesting to see how the OS distribution works out. [TechCrunch]
AdAge: AOL to Sell or Shut Down Bebo in 2010
Move Comes Just Two Years After Portal Spent $850 Million to Acquire Social Network
NEW YORK (AdAge.com) — AOL will shut down social-networking service Bebo if it can’t find a buyer, the company said in a memo to staff on Tuesday.
FULL ARTICLE
Who Wants In Google’s Experimental Fiber Network? (Map)
Source: http://gizmodo.com/5504381/who-wants-in-googles-experimental-fiber-network-map
The short answer? Everyone wants in Google’s experimental 1Gbps fiber optic network(s). The company received “1,100 community responses and more than 194,000 responses from individuals” after announcing their plans. And these requests span the entire US.
(Each small dot you see on this map represents the response from local government. Each big dot represents communities with over 1,000 respondents.)
Some may see this map as people hungry for fiber; I just see it as people hungry for Google. How many communities would beg big businesses like Walmart to set up shop, not just in some lot, but in their very infrastructure—especially knowing that whatever service there is will be in beta? [Google]
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.
Map of IP addresses around the world used to commit Click-Fraud
A recently disbanded click fraud ring in China racked up $3 million worth of clicks in two weeks. $3 million that we’re aware of. Just how detectable is this whole business of racking up fraudulent ad revenue clicks?
That intricate mess of lines above represents a portion of DormRing1, the click fraud bunch that was caught in China. The lines show the relationship of some of the IP addresses involved in the fraud and how they are connected to some fraudulent ad clicks. The whole network actually “involved 200,000 different IP addresses and racked up more than $3 million worth of fraudulent clicks across 2,000 advertisers in a two-week period.” Impressive and scary at the same time.
The trouble is that no one really knows how much ad revenue DormRing1 collected before they were caught. Click-fraud monitoring services such as Anchor Intelligence, the ones behind this catch, are evolving to keep up with the scale on which these rings are operating. It’s still difficult to judge just how well they’re doing as they’re having to infiltrate forums and gain the trust of the perpetrators in a manner reminiscent of drug busts. But as the criminals are getting more elaborate, the investigations are too.
That good news aside, do me a favor: after you read this post, comment, and all that jazz, refresh the page a few times and—Ah…I mean, heh…just kidding. [Tech Crunch]
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What is Web 3.0? Characteristics of Web 3.0
2009 06 16 What Is Web 3.0 – Presentation Transcript
- What is Web 3.0? Dr. Augustine Fou June 16, 2009. June 16, 2009.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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Good news for Android users who are miserable due to the limited game selection on their devices: Social gaming network OpenFeint is coming to Android and it’ll hopefully encourage development of more games for the mobile operating system.


