The Real-time Web (RTW) is buzzing all over the place, but I’m amazed by the amount of different interpretations of it. The RTW – or better said: large scale activity streams – are indeed reshaping the entire business and technology landscape, but how? This will be the first post of a series of articles that will be centered around this topic.
Micro-blogging (status updates) are the most visible form of the RTW, but in parallel we already had things like: RSS feeds, social bookmarking and video favorites, etc. Combine them all together and you have your first lifestreaming services. It’s important to note that lifestreams are the first form of activity streams that have more meta-data than for example a Twitter stream. A lifestream can for example include tags about a certain video that was favorited.
Instead of using the buzz word RTW, I’d prefer using the term Activity Stream. Why? Because the real-time aspect is only one half of the story. The important part is the ‘activity’.
Because of several intersecting trends, we have now arrived at yet another paradigm shift that will radically change business and technology.
The Attention Economy
Twitter is very cool and all, but can it be monetized? Hell yeah!! It’s like in the 19th century when oil was discovered: what the hell do we do with all this black stinky stuff? Well, it burns, so what? Activity is the new oil and Twitter will be the new ExxonMobil. Now it’s time to invent the appliances that run off of it.
In books about Google, like John Battelle’s The Search, the big take away is that Google is uniquely positioned between supply and demand. Well, they are, but they’re still very much in the middle. Like in most of their products, Google misses out on the ‘social stuff’. Having a direct activity communication line with demand is what Google is missing. In the Twittersphere, the dynamics are such that the supply trickles up to the demand (the people) in a very targeted way, much more targeted then Google Search.
This is where the true Attention Economy takes shape. When people explicitly state things about their activities, they leave an attention trail. This trail can be used to actively push relevant services to people.
But there is a caveat, in order to start using this ‘attention trail’, we need two things:
- We need a lot more activity. Right now we only have activity going out that is highly explicit, like the favoring of a picture. But an attention trail should include much more activity events, like “How many seconds did you spend looking at this picture?”. Right now, most systems have separate mechanisms for measuring this implicit attention data (i.e. analytics), but one can imagine that this data will be highly desired to do internal content recommendation.
- In order to properly analyze the attention trail, machines need to be able to understand each individual event. For example: “What was on the picture?”. One way of accomplishing this is by having proper meta-data on activity events. Something I have been struggling with in my Kakuteru project and the very thing that prompted me to build my own aggregation framework.
The Semantic Web
Data mining – the extraction of structured data from text – has been around for a long time. Over the past years several web companies have started doing this by means of open APIs, most notably Zemanta and OpenCalais. But interestingly, someone at Zemanta told me that most of their API usage is for extracting facts from micro-content like Tweets (so I imagine that they will adjust their product accordingly). Unfortunately, many view extracting facts from micro-content as more challenging than large formal texts. In a way it is, because a Tweet for example has a lot of slang and abbreviations in it, but you can make things a lot simpler by mixing in nano-formats. You can for example imagine that a hash-tagged tweet like this is easy for a machine to parse:
“I’m boarding my #flight KL862 to Amsterdam”
So if you combine status updates with the extraction of structured information, you can already imagine an Agent or Twitter-bot providing some useful information in return. In this case for example, it can provide information about the weather in the destination city. Or it could direct-message you if people in your social graph are boarding the airplane as well. These Semi-Intelligent Agents can be considered the first step towards the reasoning stage of the web.
The Synaptic Web
This word came on my radar the first time I was looking at a stream integrated commenting plugin for WordPress called JS-Kit Echo. I think the word accurately describes what’s happening: the neuronification of the Web (thanks @mcmurrak). Futurist Kevin Kelley has written about this phenomenon as well. In essence, the connected nodes (people and computers) are an emergent global organism. But very concretely, the word “Synaptic Web” captures all important properties: ubiquity, meaning and interconnectedness (social).
So what is this Synaptic Web, and how will it change everything? Well, that remains to be seen and it’s something I plan to elaborate on in many articles to come. But here are some appetizers for thought:
- Google’s Adwords were a very effective form of advertising because they were so targeted, but what if advertisements become so targeted that they are essentially offers/solutions provided to you? Will we still think of them as advertisements? How much are these ‘advertisers’ willing to pay for an advertisement that is 90% likely to trigger a full engagement with the consumer?
- Demand for something specific is always temporary. When a big clustering of needs is ‘detected’ small nano-companies can be formed to fulfill these needs until they are gone.
- Disambiguation is something that has been key to innovations in semantic technologies and will be very important for the Synaptic Web. Are we talking about the same thing, place, person? Machines need to be able to understand these things and serious innovation is needed here.
- When machines are able to translate a concept like ‘NYC’ to a specific location with coordinates, they might become an important tool in quickly transferring concepts to each other. So instead of communicating in text, audio or video we could communicate in actual concepts. Of course this all depends on how we interface with the web.