The other day I was thinking about hashtag line graphs — charts that show traffic on a particular topic over time — and how to make them more interesting. Visualizing traffic around a hashtag over time usually tells the story that everyone already knows i.e. some huge event happened and people started tweeting about it. Not terribly surprising.
Other experiments into semantic analysis of tweets have tried to characterize what this conversation is about. The Guardian’s riot rumor visualization is one great example but it has some high barriers to entry even if you have the crazy datavis chops. First, you need a huge sample of tweets to analyze and you’ll have trouble getting that unless you’re Twitter white-listed or want to pay a company like DataSift. Second, to ensure accuracy, you need to either build a semantic tagger more advanced than what’s currently out there, or get a bunch of people to make sure your semantic analysis coded each tweet correctly and correct mistakes. So you need manpower.
So what story could you tell if you’re not a huge paper?
Clearly identifying the meaning behind a sentence has some barriers but what about individual tags? What if you could chart how the audience around an event shifted by looking at the evolution of tags around a single topic? Surely, sheer tweet volume will tell you something about how popular an event is that but it could be confounded if a spike in traffic is from a small group tweeting a hundred times as fast as opposed to a hundred times as many people tweeting at the same rate. (Yes you could use network analysis to get picture of audience but again you need all that data.)
Like with most data visualizations, stories in data start to come out when you can mash together different datasets.
When Occupy Wall Street started, I remember the hashtag began as the cumbersome #occupywallstreet because know one knew about it. I briefly saw an #occupywallst and now #ows is the clear choice. The question is, when did Occupy Wall Street become commonplace enough where people were comfortable just referring to it by #ows?
In other words, by looking at hashtag evolution could you see the moment when an obscure march became part of the national discourse?
Let’s use Trendistic. Tumblr won’t let me embed the graph so click on the image to see the interactive version, or click here (Trendistic doesn’t display this data forever so depending on when you’re reading this, the data from fall 2011 may be gone. But the image below remains!)
I put together this storify today aggregating instances of aggressive police force at Occupy protests across the country. As I write in the intro:
The story about national Occupy movement has now become as much about aggressive police tactics as it is about its original focus of income inequality. Videos or photos of non-violent protesters being pepper sprayed or beaten have come out of almost every single march or day of action and a group of professors at the Columbia Journalism School (my alma mater) recently sent a letter to Mayor Bloomberg protesting the arrest of so many journalists this week.
Most of these photos we’ve already seen — but in the constant cycle of digital news, I think it’s useful to have a place where these images don’t get buried in our twitter feeds or home page carousels and fade from public view. I’m also including links to followup articles where available and hopefully this could serve as a list of stories to be reported more fully. If you have links that are missing from this list you can tweet me at @mhkeller or email me at: michael [dot] keller [at] gmail [dot] com.
[Update] Apparently and to much more acclaim, AdBusters decided to do the same thing and is the featured story on storify. Oh well. I’ll keep updating mine and including followup reporting and stories.
Click the “Read More” tag to view the embedded storify.