According to Ben Zimmer, I'm writing from the front lines. But it's pretty quiet here, sitting at home in Texas, looking at tweets that have come out of Libya in the last couple of weeks. And somehow I don't think I'll be the first twitterologist to suffer from combat fatigue. Maybe that's because my students Joey Frazee and Chris Brown, together with our collaborator Xiong Liu, have been the ones doing computational battle in our little research team. That and the fact that nobody is firing mortars around here.
Yet quiet as it is where I'm sitting, it's a startling fact that today it's easy to hear far off clamor, to listen to the online noise made by thousands of ordinary people. Ordinary people in war zones. What are those people thinking?
That's the basic question. What are all those people thinking? One can't help wondering. And yet the wondering doesn't lead anywhere. All the social media chatter is tantalizing, but if you're anything like me, plugging into it just gives you a headache. Lots and lots of voices, no easy way to tell who or what matters, no easy way to pick out a tune above the cacophony. No easy way to even understand the languages of most of those people directly affected by wars that are raging.
It's against that background that a group of us have been studying ways to make sense of large amounts of language data generated by people on the ground in Libya. If you're interested in details, I've made a very preliminary report available here. We've been using a straightforward three step process: (i) get lots of tweets in Arabic from roughly the right area, (ii) machine translate them, and (iii) analyze the results by counting various types of words that occur in the translations. A crude process, but it has the obvious advantage of using only tools that exist right now. And it gets results.
To see what I mean, look at the following graph. It shows how the use of positive and negative emotion words in about 5000 tweets changed in Libya just over a week ago: the higher the peak, the more positive the overall sentiment. Without me telling you any more, you can see from the graph exactly when something remarkable happened:
You won't be surprised when I tell you what event coincides with the most obvious peak in positive emotion as well as in volume of tweet traffic: Gaddafi's death. Specifically, the vertical dashed black line marks the time when news of Gadaffi's capture and death were first made public. And I'm just realizing how much I like the way that Joey, who made this graph, marked the high volume periods of tweets in red. The deposed leader's blood seems to drip from the sharp tip of the Libyan people's joyous cries. (For caveats, more facts, and less fervid embellishment, see the report!)