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Category: social graph

Day trading the information stream: Reading and Writing

Published April 1, 2008 by cgerrish

Our filtering of information pouring off the network is starting to resemble the activity of a NASDAQ market maker. A market maker is a buyer and seller in a set of tickers on the electronic market. She’s always looking for pools of liquidity, ways to match up a buyer and sellers in whatever trading or crossing network that provides the acceptable transaction.

We are buyers and sellers of information. Techmeme, Delicious, Twitter, Google Reader, Technorati, The Gang and NewsGang, The NY Times, MSNBC, YouTube, iTunes, Facebook, MySpace, CNN, your favorite Blogs, Meg Fowler, Chris Brogan, KR8TR, Karoli, C-SPAN, The New Yorker, The Public Library, News.com, TechCrunch, Mahalo, Google News, Yahoo News, ESPN, Digg, TWIT, Your personal network, and Your friend’s networks are all pushing information into the marketplace. You choose what to buy. You also sell your own writing, photos, music, films, radio into the networks you have access to, the pools the provide the most liquidity.

Just like a Hedge Fund, or a portfolio manager, we try to put together the best portfolio of feeds, and pick the best stories and pieces out of the stream. The term we hear these days is “curator” or “editor.” But the sense of time is not of the long term investor, but rather of the day trader or the market maker.

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The Precise Ambiguity of @megfowler ‘s definition of Twitter

Published March 31, 2008 by cgerrish

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

Published February 23, 2008 by cgerrish

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.

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