Turn on the Twitter firehose

If you want to see what a digital mob looks like, try following a Twitter hash tag as an emotional event occurs.

Tweetdeck - image of tweets

Tweetdeck – image of tweets two days after the events in this story

This week, I followed the hash tag #gosnell when word broke that the jury was about to return a verdict in a sensational trial after several days of deliberations. Kermit Gosnell, a doctor in Philadelphia who had operated an abortion clinic accurately described as a house of horrors, was accused of brutally killing babies who had been delivered alive. The jury found him guilty on multiple charges; a hasty plea deal resulted in consecutive life sentences.

The damburst of tweets began immediately after the announcement that the jury had reached a verdict. The tweets appeared and ran down the timeline so quickly I could barely make out the words. In the hour that passed before the verdict became known, the flow of tweets accelerated as comments incited others to comment, in a cascading effect. Given the nature of the accusations and the disgusting details that had been made public during the trial, it’s no surprise that many of the comments were harsh in the extreme, demanding retribution. As soon as one reporter announced a summary of the verdict—guilty on three first degree murder charges—the detail was echoed in thousands of tweets per minute.

I also followed the hash tag #bosma when it was announced that an arrest had been made in the case of the disappearance of Tim Bosma, a young Canadian man who had fallen among thieves. The flow, proportionally slower than the Gosnell flow, still ran at a fast pace for hours.

What was the experience like?

  • At the peak of the flow, my eye could make out only random words from a message as it slipped down the page; because of the density of messages, the random words built of coherent picture of the prevailing sentiment.
  • The topic could shift and reorient like a flock of swallows; as soon as a fact or rumour was reported, the follow-up tweets absorbed and built on it.
  • Twitter was ahead of the main news outlets by several minutes; presumably,  the process of verifying information and composing readable dispatches accounts for the difference in timing.
  • The tweets on these occasions were expressions of emotions, not eye-witness accounts of unfolding events, so they contributed very little to the understanding of the events.

In both cases, Twitter’s main strength—immediacy—was negated by the sheer volume of tweets. So many people commented that no one could read the messages as they sped by; it would take a software solution to count and categorize the sentiments and expose the various threads of argument and commentary.

Given the nature of the events being commented upon, it shouldn’t be surprising that the nasty comments demanding retribution were the most prominent. Comparatively few spoke of mercy for the accused. The flow of tweets has been likened to a fire hose; in this case, it would have taken a fire hose to wash away to stain of anger and nastiness that spread with the succession of comments.

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