Openinfluence is an open-metric developed at Paradigmalabs and tries to define the relevance of each user in Twitter. It is open because you can see the formula and contribute to improve it. You can see the formula in the picture below:

As you can see, the formula has two main components "Popularity" and "Influence". Popularity is related to static properties of your social network. It's some kind of "potential influence", the beforehand capability of getting your tweets spread.Influence is related to the propagation and repercussion of each of your tweets, the effective reach of your messages.

We have applied successfully this metric in several analysis, e.g.: during the Andalusian elections campaign or UX Spain Conference.

Currently we can represent this formula with the next plot:

We are involved in trying to improve this metric, because the two main parts have the same weight in the formula. However, is this metric more related to Influence? Is the formula below better?

We have tested Openinfluence with the next dataset. In the picture below, you can see the number of followers degree of each user in the sample (in logarithmic scale):

The correlation between Popularity and Influence (dataset) shows that the main stream of people has more or less the same Popularity and Influence. By means of the structure of this formula, some users have 0 of influence and n>0 popularity however they have not null relevance.

Suggest us your point of view !! We are expecting to improve it!!

Tell us what you think.

Send.

Comments are moderated and will only be visible if they add to the discussion in a constructive way. If you disagree with a point, please, be polite.