Data visualisation has been my pursuit of choice over the past few weeks, this has involved a lot reading, trying to teach my self how to draw and eating of cake. One of the most interesting stories about graphs I've come across, involves and the diagrams she used to highlight the adverse effect of unhygienic conditions in the army.

The past few posts have taken a stroll through a variety of buzz research tips and techniques. What this post attempts to do is to grab all the things we've looked at in the previous posts and integrate them into a coherent framework for conducting simple buzz research.

Image credit: igotmebabe

Last Thursday I stumbled across an article: “Thinking Too Much: Introspection Can Reduce the Quality of Preferences and Decisions“. The article described two experiments, the first experiment concerned itself with jam (or more specifically how introspection effects our jam related decision making). During the jam experiment, participants were given the tough task of tasting different brands of strawberry jam. The experimental condition then filled out a questionnaire about why they liked or disliked the jams whilst the control condition filled out a non jam related questionnaire. Both conditions then rated how much they liked/disliked each of the jams. Finally the ratings given by the participants in each condition were compared to the ratings given by a panel of jam experts.

Previously we've taken a little look at how you can find & gather conversations about a brand, person or topic online. But once you have all this wonderful data how are you going to analyse it? Automated analysis of content may not be accurate and often it has a pre-defined structure, for example positive or negative sentiment. This is often too inflexible when you a particular hypothesis you want to test.

There are a number of methods designed to measure advertising effectiveness. One approach is to conduct surveys both pre and post the campaign and compare the results of the surveys to ascertain it's effectiveness. Sometimes participants are shown clips of TV ads and asked if they recognise them...

Methods like this have been used for a long time, but often they rely on the accuracy of participants memories.

When the little image annotation tool was launched the other week I got asked a couple of questions about it:

Cool, but er what can use it for?

I've used tools like this two ways:

• To gather comments and feedback on print ads I'm testing. Where by this has formed on part of a larger research study.
Files: 

image_annotate.tar.gz

Sometimes you come across a situation where you want to display a particular page template for a particular content type. I did this in the image annotation tool I hacked together the other week.

Now the problem is that Drupal doesn't recognise content type based page templates as standard, it's all node based. So Drupal will look the templates in the following order page-node-123.tpl.php < page-node.tpl.php < page.tpl.php.

The solution is to snake an extra page-CONTENT-TYPE.tpl.php into theme registry when a particular content type is loaded.....

Update 8/12/09

Fail

The short of it

£5 on Olly to be voted off the X-Factor tonight.

The long of it

If Derren Brown can predict the lottery using some of wisdom of the crowds shenanigans, surely we can predict the outcome of tonight's X-Factor with some of that buzz research stuff I've been posting about. The value of buzz research is something people talk about a lot. Is all this content we're monitoring actually telling us something useful, or it it just guff. To try and figure this out I'm going to run a bit of a pseudo-experiment.

One of the limitations of using something like Social Mention for buzz research is that that sources you can pull information from is limited. What if you want to get posts from a specialist forum or niche online community? This post is all about pulling information in a structured fashion from any website you want to monitor for buzz.

What are people saying about X-Factor on Facebook?

One thing I've been looking at recently is data visualisation: how it's used in market research and how it can be improved. At one end of the scale you have a 'high design' approach which aims to produce the most beautiful, clear, data visualisation possible. At the other end of the scale you have a 'pragmatic design' approach - how can you teach your average person to visualise data more effectively.