"The Bieber Effect"

For the past couple of months I've been working with the Twitter Live Stream API. A lot of the work I've been doing is with regards to analytics. One significant trend I found, I have dubbed "The Bieber Effect."

I’ve looked at analytics from my tests on a variety of topics, from NASA to Christmas, from Education to Heavy Metal bands – and all of them have one thing in common: Justin Bieber. I’ve discovered that if you monitor and search terms for long enough, Justin Bieber always rises to the top of the most mentions, the most hashtags, the most retweets, and not surprisingly...the most active fans.

“The Bieber Effect” has permeated Twitter and has become a regular statistical outlier in all the data sets collected. As a result of this, I’ve even had to add a filter to my data collection in order to remove the massive outliers generated by "The Bieb."

My advice to everyone exploring Twitter for analytics is this: You can improve your relevancy and accuracy dramatically, by filtering out Justin Bieber.

Sorry Biebs, I just don’t need you in my tweets.