We analyze the social network emerging from the user comment activity on the website Slashdot. The network presents/ncommon features of traditional social networks such as a giant component, small average path length and high clustering, but differs from them showing moderate reciprocity and neutral assortativity by degree. Using Kolmogorov-Smirnov/nstatistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distributions./nWe also study the structure ...
We analyze the social network emerging from the user comment activity on the website Slashdot. The network presents/ncommon features of traditional social networks such as a giant component, small average path length and high clustering, but differs from them showing moderate reciprocity and neutral assortativity by degree. Using Kolmogorov-Smirnov/nstatistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distributions./nWe also study the structure of discussion threads using an intuitive radial tree representation. Threads show strong heterogeneity and self-similarity throughout the different nesting levels of a conversation. We use these results to propose a simple measure to evaluate the degree of controversy provoked by a post.
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