Twitter, the very popular 140 character social networking site, has a feature called “Trends” and is supposed to capture what the most popular topics of discussion are, at any given time. When people “Tweet” about a given topic, they can insert what is called a hash tag into their Tweet. For example, if I wanted to Tweet about Richard Feynman, and I wanted other people interested in Richard Feynman to be able to find it, I could put something like “#Feynman” within my post. Twitter would then automatically categorize this post under “Feynman” and voila, people can search for it on Twitter. This is how “Trends” are calculated. If say, within a given time span of perhaps 10 minutes, a million people put the tag #Christmas into their tweets, and this would be a very popular Twitter topic and should make it into the “Trends” list. Simple enough.
Today, as I was scouring the web for everything I could find about the censoring of Wikileaks and Julian Assange’s legal status I had a growing feeling of unease that Twitter was actively censoring the discussion of Wikileaks. New Tweets filed under the tag #Wikileaks were popping up at an astounding pace. If I left the page alone for literally 30 seconds, 40 new Tweets tagged as #Wikileaks would be there when I returned. It just seemed like #Wikileaks would be one of the most popular topics and therefore be reported as a “Trend”, but I really had no way to quantify this.
But then I found an excellent applet hosted by trendistic.com. It allows one to type in whatever hash tag terms they like and it will then compile a graph of how popular that hash tag term was over the course of whatever time period you select. In theory, since #Wikileaks is not a “Trend” as recognized by Twitter, every single term that Twitter lists as a Trend should outperform #Wikileaks. However, this simply is not the case. My web programming skills are not so good as my other talents, so I’ve yet to program this in the dynamically updating form that I envision, but here goes:
