Now I'm running some text analysis on the tweets. I'll be posting code and writing up results here over the next few days. Questions are welcome!
For starters, here are words people use in support/opposition to the #OWS movement.
| Support | Oppose | |||
| love | 0.48 | homeless | -0.93 | |
| politics | 0.46 | focus | -0.9 | |
| opwallstreet | 0.43 | crowd | -0.75 | |
| congress | 0.43 | handouts | -0.74 | |
| stand | 0.4 | capitalist | -0.71 | |
| bank | 0.39 | irony | -0.59 | |
| brutal | 0.36 | called | -0.51 | |
| class | 0.32 | act | -0.49 | |
| strike | 0.31 | scanner | -0.46 | |
| poll | 0.31 | happened | -0.44 | |
| evict | 0.31 | received | -0.36 | |
| p21 | 0.31 | getting | -0.33 | |
| stay | 0.29 | quotwe | -0.32 | |
| global | 0.29 | dont | -0.32 | |
| help | 0.25 | cont | -0.31 | |
| justice | 0.23 | john | -0.3 | |
| income | 0.23 | home | -0.29 | |
| senatorsanders | 0.23 | paul | -0.28 | |
| moveon | 0.22 | hear | -0.24 | |
| occupywallst | 0.21 | weoccupyamerica | -0.23 | |
| solidarity | 0.2 | protests | -0.22 | |
| call | 0.2 | free | -0.19 | |
| cop | 0.2 | tents | -0.19 | |
| allowed | 0.17 | protesting | -0.14 | |
| peaceful | 0.16 | occupylsx | -0.13 |
Here's the same data, rendered as word clouds, so it looks artsy. This really is the same data: sizes in the wordcloud are determined by the weights of the classifier -- regression betas, for you mathy people out there. Color and coordinates are arbitrary. So these wordclouds are exactly the same info as the tables above, just presented in a more visually appealing format.
In support:
Opposed:
As I peer at these tea leaves, I see a solidarity-oriented "stand together against brutal capitalist injustice" theme in the support words, and a libertarian "quit your whining and get to work" theme in the oppose words. What do you make of it?
Caveats and details of the method
This analysis is based on 1,000 tweets drawn from Monday, Tuesday and Wednesday of this week, so some of the themes might be specific to the events of those days. Also, there was quite a bit of noise in the sentiment coding. That will probably wash out in a large enough sample, but I don't know if 1,000 if large enough. Finally, support on twitter was running about 85% in favor of the protests, so the assessments of opposing words are probably less robust.
