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Tag: twitter

Human-Computer Interface: The Simplicity of Asking and Telling

Simplicity in user interface combined with the power of the what is returned equals uncommon success.

Google User Interface

The Google interface allows complex queries with the most basic interaction.


Twitter User Interface

The Twitter interface allows publication into the social conversation stream with a user interaction that looks very similar.

One interaction is asking, the other is telling.


A Venezuelan Moment: The Gillmor Gang considers nationalizing Twitter

Jerry Rubin

It must be an odd thing to run a company in the midst of a debate around the idea of nationalizing your core technology. In a Venezuelan moment, the Gillmor Gang considers the idea that Twitter has become so important that our national security requires nationalizing its technical infrastructure. In a two-part discussion about an open mesh / cross-service dashboard mashup and the role of Twitter as a sort of fundamental glue, the question surfaced of breaking up the centralized Twitter monopoly. You can hear the conversation here:

The conversation was provoked by some ongoing thoughts by Dave Winer around decentralizing Twitter. Initially the issues addressed were:

  • Backup of Twitter user data in the event of utter failure of the service
  • An alternative venue for the moments when Twitter is indisposed.
  • Improving reliability through distributing the infrastructure to multiple players
  • Redistributing Twitter’s monopoly power to multiple players for the common good

Discussion revolved around the general principle of open source standards and how Twitter should be re-created as a standard like ethernet, SMPT, POP, IMAP, XMPP, HTTP, etc. This would allow multiple vendors to compete with products using the same base protocols. For example, many vendors compete using a common standard for email, like for having chat support to answers their online player’s concerns, give a quick solution, and sends promotions or rewards. Standards create a very useful interoperability in the case of email and web sites. Instant messenger has multiple protocols and requires debabelization services to enable conversation between platforms.

Chris Saad put forth an interesting proposal around the idea of publish/subscribe and Twitter literally as micro-blogging. His idea is to move Twitter to a model similar to that of blogging and RSS. Through a micro-blogging authoring tool, something like WordPress, an individual would publish Tweets. A group of followers who had indicated interest in receiving messages would be pushed a payload immediately on publication. A Tweet reader would be used to subscribe to the streams of various publishers.

On the Gillmor Gang call there was some confusion about the roll of RSS in Saad’s proposal. Because XMPP can be difficult to program against, Saad suggested authoring tools that output the RSS format into a gateway that would transform it into XMPP for immediate transport. The idea is to use RSS as XML, a simple transport markup that most blog authoring tools already know how to output. However this was confused with the common usage of RSS as a polling-based publish/subscribe blog syndication methodology.

In looking at decentralizing Twitter, the focus was on two aspects of the service, replicating the unique social graph Twitter creates through the ideas of following and being open to being followed; and the immediate stream of 140 character hypertext that is generated through that matrix of connections. These two elements of the service have created a rich fabric of relationship and information flow that satisfies and intrigues 80% of the users.

The stream of information can be followed in a number of ways. Most people use the Twitter web site which offers a stream through a periodic refresh and redraw of the screen. A number of Twitter clients have been created to automate that process based on a web/RSS model of updating and publication. This streaming model is the equivalent of 15 minute delayed stock quotes. The stream flows based on the polling intervals of the reader, not on the actual publication events.

Steve Gillmor has been championing the instant messenger model of Twitter consumption. In this method an instant messenger client like Google’s Gtalk or iChat is used to talk to Twitter through an XMPP server that relays the Tweets it receives as quickly as it can on the publication event. This also works on a teleputer via SMS, or as those devices are sometimes called these days telephones. This model doesn’t scale particularly well. Users like Robert Scoble and Jason Calacanis have well over 20,000 people they follow.

The consumption strategy that makes the instant messaging model of Twitter work is to follow a core group and then track keywords of interest. Tracking keywords adds people you don’t follow into your stream and provides a proper level of noise and negative feedback into the information ecosystem. This can also be accomplished through a diversified approach to following. In modern portfolio theory this is called covariance.

It’s tracking that makes a decentralized Twitter nearly impossible. Think of a 140 character Tweet as a series of space separated tags to which you can subscribe. In this model, you’re following everyone, or at least everyone who uses that particular tag. This feature radically changes the shape of the social graph underlying the information stream. Since you don’t know who might use a tag you’re tracking, the regular RSS style contract around publication and subscription doesn’t work. Track is not commonly used today, but it’s one of the more interesting features of the service.

The idea of building competitors to Twitter on the same platform, or redistributing Twitter to multiple players reminds me of the idea that New York City should be rebuilt in Ohio because it would be cheaper. Or perhaps we could distribute a little of New York City in every state of the Union. New York City is what it is because of the people who live and visit there. Building another New York City in Las Vegas doesn’t result in the phenomenon that is New York City. In a very important sense, Twitter is decentralized at its core, it is rhizomatic rather than arborescent.


The Precise Ambiguity of @megfowler ‘s definition of Twitter

Ludwig Wittgenstein

Meg Fowler threw up her hands and finally said, “This is what I do.” She was trying to explain how Twitter goes to some new users. It’s a question that surfaces naturally with the uninitiated. They examine the “rules” and the capabilities, and then answer the question “What are you doing?” But somehow that doesn’t seem to adequately represent the buzz of talk surrounding Twitter.

The first thing new users observe, once they start following veteran users is that the question about what one is doing is only occasionally answered. What are the rules they ask, what are the rules about what to put in to those 140 characters, if you’re not answering the question?

This is where words begin to fail us. How to explain all that is not answering a question? How to explain who hears and who doesn’t? How to explain the river of talk that one follows? To explain one’s experience of Twitter, is to explain one’s self. Everyone’s experience is slightly different.

Meg Fowler’s description brought to mind Ludwig Wittgenstein’s discussion of how we learn and use language in his book Philosophical Investigations. Certainly we can talk about rules when we speak of language. But that’s not how we learn and eventually use language. Rather than learning a set of rules, it’s more a case of “this is what I do,” and you must do what you do.

Asking what one should fill the 140 characters with is like asking what words one should fill one’s voice with. Many social network sites attempt to provide context and set the rules of engagement. Following rules is what machines do, not what people do. I’ve often thought of human-computer interaction as the encounter between a world purged of ambiguity with a world filled with ambiguity. Twitter thrives on the ambiguity of its purpose, it’s a machine that leaves room for the human.

And Meg Fowler, why look to her as an authoritative voice? In a medium where most of use are finding our way and learning the landscape, Ms. Fowler has filled in those 140 characters more than 11,646 times.

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Preserving the random with coarse-grained filters in Twitter

One of the frustrations people have with Twitter is its simplicity. Twitter is an authoring environment for hypertext limited to 140 characters and a method of publishing and subscribing to an almost unlimited combination of social graphs. It achieves some complexity through its API, which allows it to be mashed up with other applications. In this sense it adheres to David Weinberger’s idea of “small pieces loosely joined.”

Twitter’s simplicity means the barrier to getting started is very low, register an identity, type 140 characters and click “update.” Understanding the value of Twitter doesn’t come until later. Non-users and new users can’t actually experience Twitter. The public timeline is there as an example, but to generalize and form opinions based on this evidence would lead one solidly in the wrong direction. The public timeline could potentially be decoded, but it’s a task very similar to spending time with Humphrey Chimpden Earwicker, Anna Livia Plurabelle and dream logic of Finnegan’s Wake. Or as James Joyce put it: Here comes everybody.

Veteran users of Twitter experience something very different from the public timeline. And it’s those regular users who begin to long for more controls, more features to help them refine their Twitter experience. Generally this is expressed through a desire to configure and define groups within the larger pools of the followed and the followers. By concisely defining groups a Twitter user could get exactly what she wanted.

But getting “exactly what you want” is exactly what you don’t want. Fine grained controls and filters are generally used to focus on common interests and concerns. The result is pre-defining the message flow you receive, creating an echo chamber. Random and negative feedback have an important role the health and stability of any dynamic organic system. When Twitter only brings you what you expect, it loses its value.

Twitter will grow new features, all applications do. But what if, rather than think in terms of precision, exactness and clarity; we thought of coarseness, randomness and ambiguity. What kind of coarse grained filters would preserve the random in a users Twitter stream? The seed for this rumination was inspired by a conversation on @Newsgang Live about squelch as metaphor for filtering Twitter streams. Imagine filtering the stream based on frequency of tweets, or location of tweets. By tuning into quadrants of the Twitterverse with coarse-grained filters new voices could be discovered. So often we think in terms of signal versus noise, but when we think of noise perhaps we should take a lesson, and listen with the zen ears of John Cage.