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Dear All.

I read with some interest the "Is the tipping point toast?" article  
about Watt's critique of Gladwell influencer "hypothesis". However, I  
believe they missed the point completely.

The work by Watts (Challenging the Influentials Hypothesis) is a  
computer simulation in which he shows that the most connected people  
are not responsible for the success or not of a viral marketing  
campaign. For all of us working with real world experiments this is  
something well known and I am ready to buy it (I did almost 5 years  
ago, when I started). Influentials (defined as the most connected) are  
as relevant as other people, like "bridges" between communities, for  
example.

The problem with the "influentials" hypothesis as is presented in the  
article is that social connectivity is everything: the structure of  
the social network around an individual is the only metric used to  
define an "influential". But this is not true: an influential for  
example can be a poorly-connected person between two almost isolated  
communities. How influential is an individual is only set by his role  
played on the spreading of information (if you think about viral  
marketing campaign) and not by his social connectivity.

Thus "influence" is given by the dynamics of the process, not by the  
social network close to the individual.

For example, in our viral marketing campaigns we have also observed  
that response time of an individual to information dissemination is  
paramount to the success or not of the campaign. And other metrics  
could be implemented (size of the community close to the individual,  
clustering coefficient of the individual, etc.)

The title of the article is also misleading: Tipping-Point is here to  
stay, the problem is how do we get closer to the tipping point by new  
techniques to identify influentials

Best

---------------------------------------------------------------
Esteban Moro Egido                 | Phone: + 34 91 6248727
Departamento de Matematicas        | Fax: + 34 91 6249129
Universidad Carlos III de Madrid   | email: [log in to unmask]
Avda. de la Universidad 30         |
E-28911 Leganes (Madrid) SPAIN     | http://markov.uc3m.es
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