Ad verba per numeros

Friday, December 10, 2010, 10:15 AM
It has been a long time since my last entry in this blog: sorry, I've been pretty busy. In fact, part of that time was devoted to a particular interesting idea which was co-developed with David Brenes, Diego Fernández, María Fernández, and Rodrigo García.

To put in short a long story, the idea was to develop a physical metaphor for influence in Twitter and check for its goodness. Sure, we are aware that influence is a rather elusive concept and that there already exist a number of choices to compute authority/centrality/clout/etc in Twitter; nevertheless, there are some juicy novelties in our approach.

First of all, we have completely disregarded the user graph; i.e., all of the computation is performed by just using the tweets and, hence, it is amenable for real-time application. In that sense, our approach is quite different from PageRank or TunkRank.

Second, because this new method can be applied in (almost) real time, user's scores are truly dynamic and not just periodically recomputed.

The implications for this are clear: a picture of the evolution of a user's influence is much more informative that just a position within a ranking. Think for instance of the possibilities for brands, marketing campaigns, and public relationships.

Regarding the implementation details; if you are thinking of citations you are getting warmer. We use Twitter mentions but in a rather novel way: we treat them as a "force" able to "move" users, while the number of followers for a user is his "mass".

That way, and taking into account the unavoidable "friction", we can compute both "acceleration" and "velocity" for every user in the Twitter stream.

Therefore, in such a context, "velocity" is a proxy for user's influence while "acceleration" provides a way to detect "trending users", i.e. those users which are gaining influence so fast that they must be involved into "something".

If you are a connoisseur of Twitter you are probably aware of the so-called "Velocity and Acceleration" model developed by Jason Harper at Organic.

No details about the model have been disclosed and, so, little can be said about its relation to our method. Nevertheless, given that --unlike our approach-- Organic's model is not focused in user influence it may very well differ in many details from our model.

For the full description of the method you can read the paper "Retibus socialibus et legibus momenti -- On social networks and the laws of influence".

If you want a palatable (but thorough) introduction please check these slides.

You'll see it is a simple and elegant solution, and you are welcome to comment anything you find interesting/intriguing about the model and, of course, we encourage you to implement it.

Oh, and don't forget to tweet about this if you like it! Besides, you can reach us at @pfcdgayo (that's me), @brenes, @littlemove (Diego), @mimiru (María), and @rochgs (Rodrigo).

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