why students of social media love twitter

In the history of media, there is often a golden age of low tech, wherein some of the most valued use of the media occurs.  In photography, there is a poetry to the early daguerreotypes, tintypes, and ambrotypes that is missing in modern digital photography.  Not that there isn’t beautiful work being done now, but that somehow by working within the constraints of young media, people create great beauty in just in spite of the limitations, but because the message is allowed to be greater than the delivery.

Twitter comes with a constraint by design. 140 characters.  In 2006, Jack Dorsey (@jack) and his company’s (Odeon) board had a brainstorming session where they came up with the idea of creating a system based on Short Message Service (SMS) – the underlying concept to cellphone texting. Dom Sagolla (@dom), one of the original Odeon brainstormers wrote,

Back then, we had no character limit on our system. Messages longer than 160 characters (the common SMS carrier limit) were split into multiple texts and delivered (somewhat) sequentially. There were other bugs, and a mounting SMS bill. The team decided to place a limit on the number of characters that would go out via SMS for each post. They settled on 140, in order to leave room for the username and the colon in front of the message. In February of 2007 @Jack wrote something which inspired me to get started on this project: “One could change the world with one hundred and forty characters.”

This self-imposed cursoriness has made for some fascinating shorthand, reminiscent of teletype messages (where customers had to pay by the letter). Naturally, there are the homophones used in texting, such as “R U going?” and “I C U went”.  Even more, poetry is elicited with “hashtags”, with which writers bring focus to certain words so that they can be ostensibly indexed and searched. In a Twitter-specific patois, though, they have come to embody a whole new wit, for example:

Visited my ex-boyfriend today #whatwasithinking

Marshall McLuhan spoke of “media determinism”, wherein the medium shapes the message.  As an artist, I’d often move from one medium to another to help myself find new ways to think.  What came out of oil paint and canvas would be a world apart from what would come out of working with charcoal on paper, or from working on a lithograph stone.

Dr David M. Berry suggests that there are other elements that give Twitter a uniqueness :

“…the quality of media conversations are changing: instead of multiple, discontinuous, heterogeneous and unsystematic conversations, we now have single, continuous, homogeneous, nearly real-time updates of news, stories, lives, events and activities, all streamed through a common format that is distributed in real-time around the world.”

Berry argues that Twitter is more like a stock-ticker than any other medium!

The brevity of Twitter brings about ways of voicing ideas that wouldn’t occur in a format without those same boundaries.  The writer must say less and infer more.  And it is in the inference that magic happens – the power of suggestion.  A stage magician’s routine is dependent on that power, even illusion, just as the suggestion of a fact can be more powerful than the blatant statement of a fact.

Twitter’s constraints also mean that there are fewer variables to consider for students of social media.  There are three main approaches to these studies that I have noticed:

  1. Sentiment Analysis – for instance, when a person says, “This new Toyota is SICK”, do they mean that it’s a car that should be avoided, or that it is the next best thing since the Mustang?
  2. Subject Analysis – WHAT is being discussed
  3. Psychological – what was the motivation of the writer for writing the Tweet

And a lot of people are trying to create ways of classifying tweets based on semantics – preferably through the use of computing – some of my favorite examples include:  “Semantic Twitter: Analyzing Tweets For Real-Time Event Notification”; “Vuvuzelas & Active Learning for Online Classification”; and “Analysis and Classification of Twitter messages”.

It’s perhaps a little terrifying and exciting to think that one day we’ll be able to look up at a large screen and see emotions sweep across a whole population – like some world-wide mood ring. Right now, we’re seeing some great visualizations of how messages are flowing across large populations through social networks. If those projects in semantic analysis are realized, we’ll be able to see sentiment and intent as well.

I’ll indulge in a little more futurism in thinking that while the task is somewhat easier with the constraints of Twitter, eventually this will be feasible in other communications, and even across social networks. But it is the very simplicity of Twitter that is making the setting out on this journey imaginable.

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