15 research outputs found
Positive words carry less information than negative words
We show that the frequency of word use is not only determined by the word
length \cite{Zipf1935} and the average information content
\cite{Piantadosi2011}, but also by its emotional content. We have analyzed
three established lexica of affective word usage in English, German, and
Spanish, to verify that these lexica have a neutral, unbiased, emotional
content. Taking into account the frequency of word usage, we find that words
with a positive emotional content are more frequently used. This lends support
to Pollyanna hypothesis \cite{Boucher1969} that there should be a positive bias
in human expression. We also find that negative words contain more information
than positive words, as the informativeness of a word increases uniformly with
its valence decrease. Our findings support earlier conjectures about (i) the
relation between word frequency and information content, and (ii) the impact of
positive emotions on communication and social links.Comment: 16 pages, 3 figures, 3 table
Router-level community structure of the Internet Autonomous Systems
The Internet is composed of routing devices connected between them and
organized into independent administrative entities: the Autonomous Systems. The
existence of different types of Autonomous Systems (like large connectivity
providers, Internet Service Providers or universities) together with
geographical and economical constraints, turns the Internet into a complex
modular and hierarchical network. This organization is reflected in many
properties of the Internet topology, like its high degree of clustering and its
robustness.
In this work, we study the modular structure of the Internet router-level
graph in order to assess to what extent the Autonomous Systems satisfy some of
the known notions of community structure. We show that the modular structure of
the Internet is much richer than what can be captured by the current community
detection methods, which are severely affected by resolution limits and by the
heterogeneity of the Autonomous Systems. Here we overcome this issue by using a
multiresolution detection algorithm combined with a small sample of nodes. We
also discuss recent work on community structure in the light of our results