paper idea: it's not the power law: how statistical generalizations are confusing social modeling on the internet

In this paper, i will argue why what many people argue are power law models of social networks on the internet are usually not really that at all. By critiquing the assumptions built into the data gathering and measurement, combined with the theoretical modeled used, it is easy to see that through considering time in different ways and by fragmenting the data set, that there are much more complex and interesting phenomena involved in these power law situations that is being masked and hidden by the generalization. In the end, separating out the individual phenomena that seem to map to power law situations usually illustrate the the phenomena as a whole is not related to the power law, and that by using the power law to describe the phenomena, we end up losing much of the unique understandings that could make or break the application of technologies in this arena for a variety of purposes.

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