Thursday, May 07, 2009

Signal to Noise in Social Media, Part II

A couple of days ago I wrote about how social media can be monitored to indicate when items such as news or products are "buzzing". By applying techniques of industrial engineering such as a statistical process control (SPC) chart, we can find when an issue exceeds its normal chatter or noise level and becomes a real issue or "buzz". I mentioned how Chrysler appeared to become more than chatter on May 1st the day after they announced a bankruptcy filing. We saw a similar trend in April with Swine Flu.

Using Technorati.com data, I have been able to put the following SPC chart together for Chrysler.
A few comments about interpretation.

1) This type of chart is an exponentially weighted moving average (EWMA). The moving average is shown on the yellow line.
2) The actual data from April 1st to May 5th is shown on the blue line.
3) The red bands show the upper and lower +/- 3 sigma control limits.

In the normal sense of interpretation as long as the tracked response stays within the red bands the chatter that is going on is normal or expected. We have no special cause or event. It seems on average chrysler was already netting about 400-600 posts per day in the blogosphere (as reported by technorati.com). On April 24th something happened to indicate a result just slightly out of control. Daily posts tipped 1085 that day and were slightly outside the upper control limit. Some type of significant event had occured.

On April 23rd there was news about the US Treasury telling Chrysler to prepare for the bankruptcy filing. The increase in blog mentions was due to the nature of the news. There was peaked interest in Chrysler. A week later on April 30th, announcements about Chrysler filing bankruptcy hit the news. On the next day May 1st the blogosphere peaked at 2635 posts. Clearly five times the normal level of chatter; it was easy to see that blog writers were reacting to the news.

I am thinking this is a simple example and most people already were intelligent enough to recognize that Chrysler was in trouble. It had been discussed for months on and off in the media. What is important is that the technique here is very powerful. It could be used to detect small shifts in interest in a product or perhaps the popularity of a person or sports team.

These waves of popular interest can be tracked and taken advantage of if detected early enough.
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