The Me Meme

Brian Caldwell over on EponymousX has written a fantastic and poetic post about the Me Meme.

He writes:

Our own personal lifestreams, or "public timeline's" if you prefer, are slightly more mundane that the one from Final Fantasy, however it can still be pondered in an analogous manner. Our lifestream threads together everything that we are. Where we go, what we say, who we interact with, how we express ourselves, concepts inside artwork that we create, symbolism that we identify. All can be considered "us" or "me" in some, hopefully non-banal, way.

We say "me" a lot in our lifestreams. Not always directly. Indirectly also. Off the top of our heads. Well thought out over hours of writing and editing. At the snap of the shutter on our iPhone. While visiting at parties and gatherings. By connecting/friending/following through social nets. Generating our APML wake and bow waves through the public timestream. We are the social seed for our downstream online and offline, everyone has a built-in personal wetware network and many people let this stream filter back online, forming a personal lifestream wake.

It's a great read full of all-too-familiar names and experiences. It reminds me of the little rant we posted at AreYouPayingAttention.com.

He also makes an interesting point. If the question of 'What do you do' becomes redundant at conferences, maybe we can move on to deeper conversations more quickly when we meet?

I know that I regularly talk with people I have never met. I trust them as much as people I have known in person for years. They are my advisors, my confidants, my partners and my friends.

The social consciousness is humming now. Can you feel it? Our Lifestreams and APML files are bursting at the seams. The best is yet to come. As our reach and reflection grows, maybe so too will our influence and insight into world affairs - both mundane and monumental.

Yes I love alliteration.

Ross Mayfield is asking for Particls

Ross Mayfield, Co-founder and CEO of SocialText, is asking for:

"There is a new kind of aggregator, for more real time attention, that needs to be build to work across status services. I'm not sure if it will be built into existing news aggregators, if existing status clients will evolve into them, or it will be something new. I just know it is coming. It will leverage status service providers and Lifestreaming you find in services like Dandelife and Jaiku."

He just described Particls.

An always-on flowing river of updates in a neat little sidebar - powered by RSS.

The Facebook AttentStream

The image below is a screenshot of my facebook News Feed. It is basically my Facebook AttentStream. Mix in posts from your RSS reading list and the LifeStream of your friends who don't live on FaceBook and you're done.

The Facebook News Feed, to me, is one of their most impressive innovations. Among many things it encourages viral swarming of friends to given activities, applications and groups. I wish more applications provided one.

The goal of Particls: To provide an interface into which the status changes of your friends co-exist with the news headlines you care about in a unified, ranked and filtered river of news.

All we need now is an RSS feed of your Facebook News Feed.

Collaborative Recommendation 3.0

Every now and then someone asks 'Why don't you build Collaborative Recommendation into Particls'.

In case you don't know, Collaborative Recommendation is when a system uses the recommendation of many people to 'decide' that a piece of content is worth seeing. So, like Digg for example, if 100 people vote that something is great (vote is a word I use loosely here), then it is probably worth seeing.

There are a few answers to that question.

1. Particls is really about filtering noise out - not discovering new recommended content (even though we provide some of that functionality just to get novice users started).
   
2. There are plenty of other great collaborative recommendation services out there, stick the RSS feed into Particls and there you go.
   
3. Google's PageRank and Technorati's Authority is already a form of Collaborative Recommendation - it isn't really very new.
   
4. The next generation of Collaborative Recommendation is actually something different. Let's call it Collaborative Filtering 3.0 or... Peer LifeStreams + Personal Relevancy

You have friends (hopefully); they have lifestreams (or at the very least RSS feeds from all their social/sharing sites) - plug their feeds into Particls and we filter out the stuff you don't care about. What's left? Stuff your friends 'recommended' that you actually care about. Collaborative Recommendation done right.

If you want to add me to your Collaborative Recommendation lineup, you can find me on Jaiku

Attention Metrics and the Enterprise

Tim Bull takes up my post about Audient, Attent and Life Streams and asks how it can be applied to the Enterprise (Tim himself is responsible for adopting cutting edge stuff for his major enterprise employer).

He writes:

"I'm going to add to the call-to-arms from the Enterprise point of view. The ability to understand not just what people click on, but the attention they give to elements of the new, rich media world is crucial. Detailed information that goes beyond "IP Address loaded page X" and various derivatives of this is crucial."

He goes on to write:

"...I think we DO need a standard for aggregating attention data from all the different clients people use during a day, for the very simple reason that in Enterprises understanding what people are using and how they are using it is a crucial part of the delivery eco-system for information. It's the feedback loop that lets you know you're getting it right.

It may be useful for bloggers etc. as well, but I think the problem should be focused on the Enterprise as this is where the "real" need is (I show my bias here, but I don't believe I as a blogger need to know in great detail who looks at what, but as an Enterprise of 160,000 people globally I do need to understand where and how my information is flowing)."

I commented on Tim's post about Enterprise adoption of Web 2.0 technologies and philosophies. I will re-phrase and expand it here...

I agree that Enterprises would greatly benefit from the sort of tools and philosophies that are happening out here on the edge. The marketplace, however, does not make it friendly for startups to target the enterprise.

Most enterprises are wary of change. Even when they are open to trying new things, most (rightly) require many more features to make the solution work in an enterprise environment - which can be a cost that startups can't absorb straight out of the gate.

Even if a startup is willing to tackle these barriers, however, they have to invest in sales and support teams to generate adoption. After all that, most enterprises don't trust small startups and just wait for the big vendors to come with similar offerings.

Attensa and Newsgator are doing a great job fighting to create adoption in Enterprise 2.0 - but they are, by the nature of their target market, forced to be conservative in their implementations and focus on a lot of plumbing and command and control issues that bog down investment in innovation for end-users.

All that being said, however, it is clear that consumer technologies are quickly being adopted by enterprises because those technologies are usually more end-user focused.

There was nothing worse than the old CRM and ERP systems that ended up causing a lot of headaches for end-users because they focused on enterprise objectives rather than making someone's day better.

By breeding technologies in the consumer market and then adapting them to the enterprise, the result is much more user-friendly. They have to be. Because end-users don't have management telling them they have to use the given tool. In the end, enterprises are forced to adapt to the most popular tools. They are still working on getting IM support right.

Getting back to the original subject, however. I agree with Tim that forging an open standard for Audient and Attent Streams can have profound impacts for both the media and for business. It could be based on Attention.XML but it would need to encapsulate far more information than just clickstreams and form data.

Life after pageviews: Proposing AudientStream and AttentStream

There is an ongoing discussion about the usefulness of the pageview. Scoble has once again raised the issue as well.

I'd like to make a proposal. Why can't the tools themselves - embedded players, browsers, second life clients, readers etc report back deep Attention/Engagement metrics?

First, some background...

A Lifestream

A LifeStream is an established concept and has been talked about by a number of people including Emily Chang, Stowe Boyd and others. It is an outgoing channel/record of everything you do/produce aggregated into a single feed.

Consider though, that this is actually a stream of your Attention Data. Data that represents what you have paid Attention to in the past. Some call it an Attention Stream.

In keeping with this theme, I would like to propose 2 additional concepts.

AudientStream 

An AudientStream (An Audient is defined as someone who pays Attention to another) is a channel/record of everything you might need to pay Attention to in the future. 

A simple example of an AudientStream might be all the RSS feeds in your OPML file aggregated together. 

A more sophisticated example would be an aggregated feed of your OPML file ranked against your APML file (using something like Particls).

Unlike a Lifestream, it is a list of things you are YET to see.

Unlike just you OPML file, it might include Twitter items, email, etc.

AttentStream

This is where I think we can make an impact on the Pageviews and metrics problem.

An AttentStream (An Attent

is defined

as someone who receives Attention from another) is a channel/record of others paying Attention to you. This would be a stream of events (preferably attributed to people) that signify Attention given to you by another.

The AttentStream would come from the tools that people use to pay attention. Browsers, Readers, Embedded Players, the Flash Player, Adobe Reader, the SecondLife Client etc, etc. Because the tool itself does the reporting it can report more subtle information that can't be gathered on the server. Think of it like distributed analytics.

An example of an AttentStream might be if the YouTube player reported each time a video was played - how much of the video was played and by which user. This way authors can get Attention information about content they were involved in producing.

The information would not just include page impressions or views. It would include richer things like time spent, partial views etc.

Each tool might produce an RSS feed that can be aggregated together by existing or new metrics companies like Compete, Buzzlogic and Feedburner.

With an AttentStream one could do basic things like displaying the identity of your subscribers (those that grant permission) much like Twitter shows your followers.

It could also do more advanced things like going beyond the pageview to give you more information about who is spending time on your content with or without a click.

I would volunteer

Particls

to testbed this type of system for publishers. If the community likes the idea and we come up with a concrete implementation we will be the first to provide reports to publishers about the amount of visibility their content has received from our users who opt into providing that information.

This does not just mean just click throughs (which can already be measured with Analytics packages and Feedburner) but rather more subtle gestures like 'time spent' viewing the content via a popup alert or on the ticker. These are more subtle, yet equally important forms of Attention giving and engagement.

Join the conversation

This is just the beginning of an idea. Join the conversation and suggest some concrete implementations.

Drop me a line

if you are interested in helping out or join us on

Tangler

for real-time chat.

Update

Elias has

written a follow up

discussing the motivations behind collecting this sort of data. I have also responded in his comments to further clarify my thoughts.