This post is the first of the new Simple Science category. This thread features research topics that are mainly focused on intuitive research facts/insight detected in the most important and well known virtual communities. However, research on complex networks is very interdisciplinary thus we do not exclude to extend our findings to other than social networks.
Since we want an audience as broad as possible and the content to be as readable as possible, we will not use strong statistical tools, but instead we adopt the basic and understandable ones. In special cases where more advanced mathematical methods are needed, we provide adequate and clear explanations.
Twitter is one of the most used micro blogging platform. Every day, more than 400 million short messages (140 characters, called tweets) are posted in user’s profiles. The communication in Twitter is performed by adding in the text a user’s reference prefixed by a @ character (for instance @lpttwer). Indeed, words that begin with # have a special meaning and represent a hashtags (for example #simplescience). In the last years, hashtags have turned out to be a very powerful and effective way of tagging content, searching or detecting trending topics in this virtual community.
Because of that, we are concerned in understanding how the way users employ hashtags is changing, and in particular how the hashtags’ length is changed in the last months.
The plot on the left contains the average hashtags length as a function of time. We base our findings on a set of more than 1.2 billions tweets, collected between August 2011 and October 2012 (averaged by weeks). It is clear from the figure that the average length has increased in the last 18 months. One of the possible explanation of this phenomena is that the widely use of hashtags in online communications has limited the unused words and consequently, users have to choose longer hashtags (maybe compounds words).
However, this picture of the situation is far from being complete. In fact, we see that the standard deviation of the hashtags lengths (plot on the right) has increased too as time pass by. This captures another important feature of the hashtags’ dynamics. Not only the average length is becoming bigger, but also the variation of the lengths compared to the average is changing, meaning that the longer hashtags increase faster than shorter ones.