The Financial times has one article ("Warning: interruption overload") on the prevalence of interruptions for knowledge workers:

"The profusion of communications channels is definitely harmful for productivity, because it leads to more interruptions," says Jakob Nielsen, an expert on information technology usability. "For people doing knowledge work – the most highly paid employees – every time you are interrupted it takes 5-15 minutes to fully recapture your train of thought and get back to being completely immersed in your main task."

There is a growing body of research to support this view. When Basex, a New York-based IT research company, conducted a study on the effects of interruptions on 1,000 office workers, it found that they spent an average of 2.1 hours a day dealing with interruptions (including the time taken to "recover" afterwards).

More recently, the same company (Basex) covered by The New York Times affirms that information overload will be the Problem of the Year in 2008. In the last year, there have been a plethora of applications dealing with time management and todo lists. It really looks like people are painfully aware of the problem, and trying to design solutions to cope with information overload. Still, it’s an unsolved problem. Whoever finds an easy, generalized solution, will have a huge success.

The main issues with attention economy are as follows:

- There is too much accessible information due to technological advances and not enough attentive ability to focus on all of it at once. As a result, our attention is stretched thin and is especially drawn to those places that provide information we seek and/or are interested in.

- Attention is a scarce resource and a new economy builds on this scarcity. The strong hypothesis of the attention economy is that monetary transactions will be replaced by attentional transactions in the future.

- To succeed in today’s attention economy, businesses and websites must determine what information their users want or risk losing them to competitors.

- We provide that information by what we vote to be our favorite or by what we click on (including what websites we visit, especially on a regular basis.)

- Due to the competitiveness of gaining the customer’s attention, businesses and websites will often utilize complex algorithm programs to determine what we will likely want next, based on what we have already clicked, voted, or purchased.

- Consumers often do not know that their information is being gathered in this fashion via their online clicks. As a result, they do not have control over this private information.

- APML is a format to encode where you allocate your attention (i.e., your attention profile). This profile will be used by sites and service providers to personalize the information they present you.

- Your attention profile is portable and makes your identify available to whoever you wish. This is a stark contrast to the way your attention is mined ‘implicitly’ by most websites.

So if you read up to here, you may have thought: “well, I do have an account on most of those websites!” And you may have already created quite an abundant trace of your preferences. That’s true even if you don’t have an account on any, but simply browser around and use search engines. But do you have control over it? Can you ask the company who tracked your taste to give you back that information? And you may even write forum posts, blog posts and comments, product reviews, etc. In a word: you express what captured your attention in many ways, not just clicks. It would be great if all that information about what you cared enough to write about followed you from site to site. For example, Amazon may have a good dataset of your pages browsed and items bought… but if you go to Barnes&Noble they have to start the guessing game from scratch!

There are at least two solutions to implement a portable attention profile: attentionTrust.org and APML.

AttentionTrust.org

Many organizations have begun focusing more on attention economy than ever before. O’Reilly Media’s 2006 Emerging Technology Conference focused greatly on attention economy. The non-profit organization AttentionTrust.org was founded to “guarantee users’ rights to own, move, and exchange their attention data.”[1]

The organization was founded by Seth Goldstein and Steve Gillmor to make sure users stay in control of their own data, which has “real value and needs to be protected.” They believe that how we browse, what we say, and what we read says much in who we are individually.[2]

The key idea behind attentionTrust is that your attention has value, and that value is yours to keep. That is, attentionTrust exemplifies the strong hypothesis and gives it a veritable implementation in the form of a browser add-in.

Their organization is guided by four principles:

1. Property – “You own your attention and can store it wherever you wish. You have control.”

2. Mobility – “You can securely move your attention wherever you want whenever you want to. You have the ability to transfer your attention.”

3. Economy – “You can pay attention to whomever you wish and receive value in return. Your attention has worth.”

4. Transparency – “You can see exactly how your attention is being used. You can decide who you trust.”[3]

5. They have developed a Firefox extension for users to track their own data. AttentionTrust’s goal is for users to be able to control their data and decide whether they wish to sell it to advertisers or not. Currently, advertisers are collecting that data and using it to their advantage, often without the consumer even knowing it. This is why the World Wide Web is sometimes called the “Implicit Web.”

APML

The Attention Profiling Markup Language (APML)[4] “Allows you to share your own personal Attention Profile in much the same way that OPML allows the exchange of reading lists between News Readers. The idea is to compress all forms of Attention Data into a portable file format containing a description of your ranked interests.”

So just as anyone can share the contents of a blog with you via RSS, or an entire reading list with you via OPML, APML makes it possible for one to share a ranked list of one’s online browsing habits and interests. The format is still not very wide-spread, but big players such as Google and Digg have plans to implement it in their applications.

Michael Pick has written the most comprehensive overview[5] of the Attention Economy and APML so far.

From CleverClogs[6]: “…Attention profiles are consolidated, structured descriptions of people’s interests and dislikes. The information about your interests and how much each means to you (ranking) is stored in a way so that computers and web-based services can easily read it, interpret it, process it and pass it on should you request and permit them to do so.”

In February 2007 Web 2.0 industry analyst Emily Chang[7] detected a crucial problem with her blog post ‘My Data Stream’. Lots of comments and start-ups followed up trying to solve the problem Emily described: "As the calendar rolled to 2007, I kept wishing I could look at all my social activity from 2006 in context: time, date, type of activity, location, memory, information interest, and so on. What was I bookmarking, blogging about, listening to, going to, and thinking about? I still had the urge to have an information and online activity mash-up that would allow me to discover my own patterns and to share my activity across the web in one chronological stream of data (to start with anyway)."

She has managed to do that, and right now you can get a feed of all her activity in a convenient APML feed (see the original blog post). The challenge still remains as to how to represent just the information that matters (without leaving out anything that would be crucial for future applications). This is why APML is still an evolving format. The more applications and services use APML, the easier it’ll be to design it so it fulfills the needs of the users.

An example of a concept element in APML:

<Concept key="education" value="0.50" updated="2007-10-15T10:46:52Z" from="profiler.engagd.com"/>

This means that for the concept education to have a mild interest (.5, from -1 to 1) and that the last time the site engagd.com updated this concept was 2007-10-15. An APML file is a long string of concept keys like this one describing your interests.

You may not care about Emily’s Attention… but you do about your friend’s. What are they reading lately? Anything you may be interested in? As you see, the more people start using APML and attention profiles, the easier it’ll be for everybody to find new information and stay away from things you don’t want to pay attention to (like those Viagra ads).

The issue here is trust again: In the future, a site will hide information from you after checking your attention profile. But can you be sure that the information that you will never see is really something you want to ignore? Forever? No. Your attention profile must change with you. In fact, it must implement some things that minds have developed in the last few thousand years of evolution: forgetting. Imperfect recognition and recall. Are memories of what you visited proportional to the time you spent with them? To the surprise they provoked? To the emotions they provoked? To their relevance at that time?

APML supports the concept of profiles. The spec example includes two profiles: ‘work’ and ‘home’. This makes sense … people tend to have a different set of preferences in the context of work than they do at home.

Your attention profile

An Attention Profile is a list of the topics and sources you are interested in, and a value representing your level of interest in them. It is implemented as an APML file.

What goes into your attention profile? Though not an exhaustive list, your attention profile could be based on:

1. Pages you bookmark and tags you assign
2. Your favorite videos, music and TV shows (we saw how last.fm provides music tracking)
3. Hyperlinks you follow and share with your friends (clickstream)
4. Things you write about and topics you keep track of (e.g., your blog posts and comments)
5. Items you click on in your feed reader
6. Things you buy from a web store
7. Places you visit and events you attend
8. IM logs
9. Browser history
10. Documents, and E-mail.

Vendors (who will create APML files) must decide which of these items they mix and match in your profile. Also, the tool you use to generate the APML file will determine how weights are assigned for each concept. That means that two different vendors may have different ideas about how to weight the different concepts that form your attention profile.

This is your life. Online, but your life: conversations, things you look at, things you buy, things you share with your friends. And it can all be logged. You don’t need to trust your own brain to hold your memories. Would you like to know what music you listened to ten years ago? I mean exactly the same day (and time)? You will be able to.

How do you get an attention profile? Right now, there are services that build one for you.

Engagd.com is one of these services. For now, you can add the feeds you read (using an OPML file).

It all depends on what information you want to include in your attention profile. Some people may not want to have emails and documents included, but are fine with pages visited.

Many of the technologies that are cropping up in the web 2.0 landscape are based on aggregation: what it’s called a web mashup. ‘Yahoo pipes’ provides an easy interface so you can combine data streams from many different providers and generate new useful stuff. In this sense, an attention profile is a combination of data sources that you have already, and the final result is more useful than the sum of the parts.

One may think that existing social bookmarking services (such as del.icio.us) fulfill the role of an attention profile already. I can visit any friend’s del.icio.us account and see what he found interesting. You can think of your attention profile as your own delicious tag cloud that follows you from site to site. When sites are aware of it, they change to better adapt to you. However, the attention profile goes further: it’s not only the things you have bookmarked that matter; it’s also what you read (even involuntarily!), what you write, the music you listen to, what you buy, and whose email you open (as much as those you select as spam!).

Benefits from using an attention profile

But the most important question is, once you have an attention profile, what do you do with it? Right now, its utility is limited by the number of applications (online and offline) that can make use of it.

First, having an attention profile can improve your experience in most sites. Future APML-aware sites will select the content they send you so it fits your interest instead of serving generic, one-size-fits-all content. That is, you carry an ID with your taste, that tells the site owner: “don’t bother me with content XYZ”. The sites will also make better suggestions as to what you can visit next. Social sites will let your friends know what you are reading lately.

Attention Profiles can also be a key to precise information retrieval. Right now, search engines know nothing about your tastes (although google is aiming to solve this: see google history[8]). Imagine that you can disambiguate your queries by using your attention profile. A person who has been recently paying attention to love stories and types ‘chemistry’ is not looking for the ‘academic’ sense of the term.

How does using APML/attentionTrust help solving the privacy issues mentioned above? by giving users control over their own attention profile. The final word of who I wish to share my data with is the mine, instead of being subject to unscrupulous data-mining.

[1] http://en.wikipedia.org/wiki/Attention_economy#CITEREFAttentionTrustorg2005

[2] http://attentiontrust.org/

[3] http://attentiontrust.org/

[4] http://apml.org/endusers/overview/

[5] http://www.masternewmedia.org/online_marketing/attention-profiling-apml/apml-beginners-guide-attention-profile-20071113.htm

[6] http://www.cleverclogs.org/2007/10/basics-of-atten.html

[7] http://www.emilychang.com/go/weblog/comments/my-data-stream/

[8] www.google.com/psearch

The idea of the “Implicit Web” is based on the fact that users are defined by the websites they visit, indicating what they like, and by the websites they avoid, indicating what they do not like. In turn, users change the world by those sites they visit and those they avoid.[1]

This happens when users stop to read an online article, recommend a favorite movie to their family and friends, and play a favorite song over and over again. Yet, virtually all users never stop to think about how valuable that information is, to both us and to advertisers and companies trying to gain their attention.

Companies do not always use implicit means in order to obtain information; often users themselves will give out that information when they rate their favorite news stories, music, movies, and more. Sites like Digg.com, reddit.com, Helium.com, and others like them utilize their users’ votes to determine in what order stories should appear on their websites.

One of the main elements of Web 2.0 is the fact that users help to determine what information is most important, what movies and music are the most popular, and what products, services, and websites are considered to be the best. So attention economy and Web 2.0 are closely related to the success of popular and successful businesses and websites, as well as related to the expansion of the Internet itself, since Web 2.0 requires user feedback and continues to take the Internet by storm.

Advertisers and companies are focusing on software that uses complex algorithms to find out what exactly users like and then present options that the software believes those people would also like. Some websites already use this strategy in order to make their websites more personal to users in an effort to attain and keep their attention. Four examples are Last.fm, Google, Amazon and Netflix.

Last.fm

Last.fm is a service that provides users a plugin into iTunes and other major music players. It captured the songs and artists users chose to play, then used that information to infer what artists were their favorites.

What was revolutionary about this service though was that this site took the next logical step to keeping your attention: it introduced you to new artists and new songs that were comparable to the artists and songs you already listened to. As a user, this would make you very happy because this was all done automatically; the software would figure out the type of music and artists you liked listening to and then calculated what new artists and music best matched up with those parameters you gave via the songs you chose to listen to.

The idea is certainly a valuable one, as the CBS Network bought Last.fm for $280 million in May 2007.[2]

Amazon.com

Amazon.com has been utilizing the Implicit Web for a while now, as every time we click on a product link, it shows us recommendations on what we might be interested in based on what we click. It does this via algorithms that calculate what we would likely be interested in based on what we click.

Not only does this occur in the “New For You” section, but it also occurs in the “Recommended Based On Your Browsing History” section, as the algorithms also calculate any related items that match up or can be utilized by the items you clicked onto, and in the “Others Purchased” section, where items that are comparable to what you are looking at and that other people have purchased are displayed.[3]

It took Amazon over ten years to build and perfect a system that is able to tap into your browsing history, past purchases, and purchases of other shoppers in order to encourage you to buy products that correlate with that information. It’s even more remarkable that the system is able to “remember” what you clicked on a few minutes ago or a few years ago.[4]

Google.com

Google is another company that utilizes our implicit behavior in order to show us search results we would likely be interested in. Clicks are used as feedback for its complex, ever-changing algorithm.

The big problem with this is that Google does this automatically, without the user even knowing her information is being gathered and used. That is why Google was recently criticized for not respecting user privacy.[5]

Privacy International published a report in September 2007 that identified Google as having the lowest rankings when it came to consumer privacy, due in part to the broad spectrum of Google’s product services and their ability to share extracted data between these services, Google’s market dominance and the size of its user base, and their “aggressive use of invasive or potentially invasive technologies and techniques.”[6]

Netflix.com

Netflix rents movies over the web. As amazon, it is crucial for Netflix to anticipate what users will like and suggest it. Personalization algorithms are so important for Netflix that they offered a large dataset and their reference algorithm to beat in a public contest. Netflix provided 100M ratings (from 1 to 5) of 17K movies by 500K users. These essentially arrive in the form of a triplet of numbers: (User,Movie,Rating). They hoped to attract the most talented machine learning people that way. An increase in a few percentage units in predicting the client tastes makes a big difference in the average number of movies rented.

The fact is that Netflix, like any major corporation cited here, they kept as much of their user’s attention traces as possible, and have a research department worried with exactly one thing: how to use that information (the cloud that you left behind) to make more money.

One criticism is that Google, Amazon etc are using your attention as if it was their property. In fact, Google determines what you will read (ads included) every time you click on their search button! And Google gets cash as an advertising company more than anything. Of course, if their algorithm brings up only content that has paid to be there, people wouldn’t use it… So Google walks a fine line between offering relevant content and profiting from that content. Some content may be less relevant and more profitable. Every single page online plays that game: some sell links to less-relevant content, but attract users.

Wikipedia mentions that “The paid inclusion model, as well as more pervasive advertising networks like Yahoo! Publisher Network and Google’s Adsense, work by treating consumer attention as the property of the search engine (in the case of paid inclusion) or the publisher (in the case of advertising networks).”

The fact is that there is a lot of information about us already out there ready to be mined. And this brings up privacy concerns. Most social web 2.0 sites have users displaying very personal information. Bloggers give you details of their lives you don’t want to know. You can have full access to demographics and bio of mostly anyone with a facebook profile. And there are services that specialize in aggregating those bits and pieces about you (and maybe sell them to the highest bidder).

On August 4, 2006, AOL released a compressed text file on one of its websites containing twenty million search keywords for over 650,000 users over a 3-month period, intended for research purposes. AOL pulled the file from public access by the 7th, but not before it had been mirrored. AOL apologized for releasing the data into the public domain. Even though AOL used numeric codes for each user, discovering the identity of some users wouldn’t be that hard–if someone wanted to try. That’s because people search on their own names (ego surfing) and also search telephone numbers, social security numbers… and names of (ex)loved ones. The New York Times (Barbaro & Zeller, 2006) [8]published an article that proved that one could reconstruct quite a lot of information from the internet searches AOL published, and that this was an implicit risk. The key factor here is informed consent: the NYT-interviewed user was right in that “[She] had no idea somebody was looking over [her] shoulder.” Worse, users felt they had no control over those data.

Note that AOL’s users did not sign their consent for these data to be collected, and they did not have direct control over which data was stored. They often did not know that such data were being collected.

AOL uses google as their search engine backend, so google has at least as much information about you as AOL had about their users when this database was released.

The basic idea is giving users the power to administer their attention profile instead of leaving it behind for any company to profit from it. Plus, the information that is ‘already there’ or ‘already being collected automatically’ does not have user’s consent (or this consent is implicit at best). If users could encapsulate their attention profile, then anyone wanting to use it would need to ask for explicit consent.

Of course, the danger of consolidated information in one place: if an attacker breaks into your attention profile, he will have more data available than any individual organization has now (including most governments’ intelligence sections). However, it could be that we are actually safer this way. From Clevercogs[7]: “The privacy aspect of attention profiling is brought up quite often when I talk to people. They consider their [attention profile] as their private property and are usually afraid their browsing behavior will be exposed to prying eyes. I look at the privacy aspect of attention profiling from a different angle: right now sites like Facebook and Google collect usage data from and about me. They know about my interests, they know what sites I open and they know who my friends are. At the moment all this is a one-way operation: they collect the data that I give to them and I get no insight as to how they filter the content they or their advertisers offer to me. I prefer to have that information distilled into an attention profile so that I can at least have control over whom I share this information with.”

The important thing here is who has the control: the companies (as it is right now), or the user.

[1] http://www.readwriteweb.com/archives/the_implicit_web_lastfm_amazon_google.php

[2] http://www.readwriteweb.com/archives/the_implicit_web_lastfm_amazon_google.php

[3] http://www.readwriteweb.com/archives/the_implicit_web_lastfm_amazon_google.php

[4] http://www.readwriteweb.com/archives/recommendation_engines.php

[5] http://www.readwriteweb.com/archives/the_implicit_web_lastfm_amazon_google.php

[6] http://www.privacyinternational.org/article.shtml?cmd[347]=x-347-553961

[7] http://www.cleverclogs.org/2007/10/basics-of-atten.html

[8] Barbaro, M., & Zeller, T. J. (2006). A Face Is Exposed for AOL Searcher No. 4417749. The New York Times.

Note: we will run a series on attention economy because we think it’s a very important concept to understand for the average internet citizen. It also helps understanding why we design what we design

According to Wikipedia, attention economy, or attention economics, as it was first known, is “an approach to the management of information that treats human attention as a scarce commodity, and applies economic theory to solve various information management problems.”[1]

It can also be described as “a marketplace where consumers agree to receive services in exchange for their attention.” This includes personalized news (such as news feeds,) personalized search (such as Google,) and alerts and recommendations to buy (such as Amazon.)[2]

The basic concept is that an economy develops around a scarce resource. This resource may have been food, energy, or information in the past. Agriculture, industrial revolution, and telecommunications (mainly internet) displaced what was the key scarce resource. However, in current internet-saavy societies, information is no longer scarce: there is plenty of it. The problem is mostly which information is worth our time attention, or trust. The issue of what information to trust and how people judge how trustworthy each piece of information is online is a whole other interesting topic. In this report we will focus on the birth of the attention age or the attention economy, and how it affects the average person online.

There are two main uses of the term ’attention economy’. We will call them the strong hypothesis and the weak hypothesis. The strong hypothesis is that "attention transactions" will replace financial transactions as the focus of our economic system (Goldhaber 1997, Frank, 1999)[3][4]. The weak hypothesis is a lot less pretentious in its claims: managing attention is better than managing other resources such as time and effort. The book by Davenport and Beck (2001)[5] is a good example of the weak hypothesis. It is important to decide which of these two hypotheses you want to operate with, since the strategy you use will be completely different. In this post series on attention economy, we will use the strong hypothesis.

Whether you are an end user of the media, a content producer, a marketer or a business man, you need to start thinking about the value of your attention and that of the people you interact with (your peers, your prospects, and your clients).

Herbert Simon is often credited with being the first person to describe what attention economics is – that a wealth of information leads to a dearth of attention due to the fact that there is so much information out there and only so much attention that can be given to information, and the idea behind rationalizing how much attention any one information source receives.[6]

Golhaber (1997) seminal paper (on an online peer reviewed journal) is however the crucial turning point for this idea. This article presents the strong hypothesis and its consequences. In what follows we will try to introduce the idea of attention economy, mostly from Goldhaber’s point of view and how some popular pages implemented attention technology. Goldhaber has been preparing a book for the last 10 years, and he blogs prolifically.

[1] http://en.wikipedia.org/wiki/Attention_economy

[2] http://www.readwriteweb.com/archives/attention_economy_primer.php

[3] http://www.firstmonday.org/issues/issue2_4/goldhaber/

[4] http://www.heise.de/tp/r4/artikel/5/5567/1.html

[5] Davenport, T.H., Beck, J. C. (2001) Attention Economy. Harvard Business School press, Boston, MA

[6] http://en.wikipedia.org/wiki/Attention_economy#CITEREFSimon1971