Tuesday, July 17, 2012

Media Noise Unit

There was a recent French article in Les Echos on a new concept aimed at measuring media intensity on news events: Media Noise Unit (unité de bruit médiatique in French).

The article is in French so I'll only summarize the gist of it. Three doctorate students derived this new metric to measure the intensity of an event across all media types (TV, print, radio) and aggregated into a single value based on the audience reach. So in a given day the French Elections might have an overall intensity of 412 for instance. The number in itself isn't important, it's how it compares to other events that same day and over past periods that is.

The main learning from a wider analysis based on this new metric is easily summarized: modern media tend to spend more and more time on less and less topics, what the researchers labeled "media craze". In a given day, only a handful of events will have very disproportionate intensity compared to the other topics, and the trend has sharpened over the years. The top "noise-makers" today makes twice as much noise as the top ones from 5 years ago.

                                                Noise units went through the roof in 2011

The article goes to describe the paradox behind this: in a world where access to information is getting wider and richer, we are being saturated with just a few events. However, the article goes on to note, humans have been proven to only remember up to three news events in a day, two of which they will forget within the next 24hours...

I found the article very interesting from a analytical point of view: I am especially fond of techniques that bridge the qualitative-quantitative gap. Being able to measure all the activity around a given topic seems like a very difficult task, but it enables many interesting insights to be driven afterwards. Comparing the intensity of the hotness topics over time is pioneering work!

But I am also a little skeptic about this technique and metric which seem almost to good to be true. Even a human would have a hard time labeling an article as being part of a certain topic or not, how well can this task be automated? During the French elections, where all articles where one of the candidates' name appeared be labeled as part of "french elections"? But what about routine actions the government did at that time, or visits abroad from the president? And certain events will trigger other more general analysis and essays that apparently have little to do with the original event. Are these counted or not?

I also saw no mention of online media which is a growing source of information. Restricting to TV, print and radio might be introducing some self selection biases to the analysis.

Anyway, even if not perfect there are some interesting insights. I would actually be very curious if these insights hold outside of France...

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