Truthy is a system to analyze and visualize the
diffusion of information on Twitter. The Truthy system
evaluates thousands of tweets an hour to identify new
and emerging bursts of activity around memes of various
flavors. The data and statistics provided by Truthy are
designed to aid in the study of social epidemics: How do
memes propagate through the Twittersphere? What causes a
burst of popularity?
A friend and I started playing around with twitter data back in early 2010. We currently have something close to over 587 million tweets collected (We stopped collecting earlier this year). We only pulled English tweets and those that described what someone was feeling (Im, I am, I feel, I am feeling, etc. along with the negatives I don't feel, I do not feel, etc).
We were able to see some interesting events happen during the time though. This is a graph of the anxiety levels of twitter on March 11th, the bottom axis is the hour of the day EST. The earthquake hit Japan @ 1:46 EST.
Out of curiosity, is Twitter data such as this freely available to anyone, or was this specially acquired for this set of students? I can imagine a number of interesting projects that might arise out of such a data set.
I've also been collecting twitter data for a bit. I developed a heatmapping application that runs on the GPU to produce time-animated heatmaps in real-time for any user-generated query over a Solr database of hundreds of millions of geotagged tweets. You can see a rough demo at http://youtu.be/4_v2EZGiA7w . Hopefully I'll release it as a web app when I get time this summer.
Wow... This is awesome. I actually did a project for my high school science fair that focused on analyzing twitter. It was no where near as sophisticated but it really opened my eyes to the massive amount of data and the availability of commodity hardware that can actually handle terabytes of data.
But, this is because non-English tweets that we have discarded are much more frequent during the night in our time zone, and they often don’t contain the word ‘a’ as often as English tweets do.
This doesn't make sense; are they only discarding the non-English tweets during certains times?
Hi, if you like that kind of stuff, I might give you an intro with Peter Gloor, who is author of swarmcreativity.net and at the MIT Center for Collective Intelligence. Tag #Twitter, Stock Prediction, Mood etc. You might meet on campus :)
Interesting data. I would be curious to find out how the general sentiment correlates with consumer behavior, e.g., financial market swings, purchases on amazon.com, google searches, etc.
[+] [-] Anon84|14 years ago|reply
http://truthy.indiana.edu
[+] [-] arashdelijani|14 years ago|reply
[+] [-] TravisPe|14 years ago|reply
We were able to see some interesting events happen during the time though. This is a graph of the anxiety levels of twitter on March 11th, the bottom axis is the hour of the day EST. The earthquake hit Japan @ 1:46 EST.
http://i.imgur.com/BeBwa.jpg
There is a strange dip around noon that we are unsure of how to account for as our servers did not report any failures.
It was a fun project to play around with.
[+] [-] Anon84|14 years ago|reply
[+] [-] cpeterso|14 years ago|reply
Maybe people are away from their computer at lunch.
What do the blue and green line colors indicate? It would also be interesting to track emoticons. :)
[+] [-] Permit|14 years ago|reply
[+] [-] Anon84|14 years ago|reply
I was lucky enough to get it more than two years ago and have been accumulating data ever since.
[+] [-] tmostak|14 years ago|reply
[+] [-] seeingfurther|14 years ago|reply
[+] [-] akshaykarthik|14 years ago|reply
[+] [-] joejohnson|14 years ago|reply
This doesn't make sense; are they only discarding the non-English tweets during certains times?
[+] [-] arashdelijani|14 years ago|reply
[+] [-] jermaink|14 years ago|reply
[+] [-] arashdelijani|14 years ago|reply
[+] [-] grout|14 years ago|reply
[+] [-] akg|14 years ago|reply
[+] [-] Anon84|14 years ago|reply
[+] [-] roarktoohey|14 years ago|reply
[+] [-] tzm|14 years ago|reply
[+] [-] mrlinx|14 years ago|reply
[+] [-] christiangenco|14 years ago|reply
[+] [-] arashdelijani|14 years ago|reply
[+] [-] molsongolden|14 years ago|reply
Ahhhhhhh