How to read this figure: The 2nd top-down subfigure indicates that users with very few unique @-mentions (those who mention a very limited set of other accounts) have an impact score approximately identical to the average (red line), whereas users with a high number of unique @-mentions tend to be distributed across impact scores distinctively higher than the average one.
Here's a snapshot taken from our paper "Predicting and Characterising User Impact on Twitter" that will be presented at EACL '14.
In this work, we tried to predict the impact of Twitter accounts based on actions under their direct control (e.g. posting quantity and frequency, numbers of @-mentions or @-replies and so on), including textual features such as word or topic (word-cluster) frequencies. Given the decent inference performance, we then digged further into our models and qualitatively analysed their properties from a variety of angles in an effort to discover the specific user behaviours that are decisive impact-wise.
On this figure, for example, we have plotted the impact score distribution for accounts with low (L) and high (H) participation in certain behaviours compared to the average impact score across all users in our sample (red line). The chosen user behaviours, i.e. the total number of tweets, the numbers of @-replies, links and unique @-mentions, and the days with nonzero tweets, were among the most relevant ones for predicting an account's impact score.
In a nutshell, based on our experimental process, we concluded that activity, interactivity (especially when interacting with a broader set of accounts rather than a specific clique) and engagement on a diverse set of topics (with the most prevalent themes of discussion being politics and showbiz) play a significant role in determining the impact of an account. For a more detailed analysis, however, you'll need to read the actual paper.