Filed under: Data, Twitter, Visualisation | Tags: Data Visualisation, Twitter
An oldie but a goodie: Check out this cool tool that you can use to visualise twitter consumer data.
Trying searching on your own company brand or your clients and see how active they are within the twitter community.
What are the most appropriate advertisements to maximise click-through rates on a particular web page? What are the most relevant search results for a particular query?
These questions may seem simple enough, but coming up with the perfect answer is a problem of staggering proportions. Why? Because the data is often high dimensional, too sparse or too noisy to make intelligent decisions.
For instance, there are billions of interactions that go on between web pages and advertisements, but the vast majority of interactions happen so infrequently. This makes it very difficult to learn from them.
An advertisement for a mom and pop pizza joint, for example, may appear on a particular page only once or twice. And what if no one clicked on that ad? Does this really mean the click-through rate is zero? Or is it possible to learn more about these types of interactions even when the data is so limited?
More from Yahoo Research: http://research.yahoo.com/node/89