568 research outputs found
Standing Swells Surveyed Showing Surprisingly Stable Solutions for the Lorenz '96 Model
The Lorenz '96 model is an adjustable dimension system of ODEs exhibiting
chaotic behavior representative of dynamics observed in the Earth's atmosphere.
In the present study, we characterize statistical properties of the chaotic
dynamics while varying the degrees of freedom and the forcing. Tuning the
dimensionality of the system, we find regions of parameter space with
surprising stability in the form of standing waves traveling amongst the slow
oscillators. The boundaries of these stable regions fluctuate regularly with
the number of slow oscillators. These results demonstrate hidden order in the
Lorenz '96 system, strengthening the evidence for its role as a hallmark
representative of nonlinear dynamical behavior.Comment: 10 pages, 8 figure
Twitter reciprocal reply networks exhibit assortativity with respect to happiness
The advent of social media has provided an extraordinary, if imperfect, 'big
data' window into the form and evolution of social networks. Based on nearly 40
million message pairs posted to Twitter between September 2008 and February
2009, we construct and examine the revealed social network structure and
dynamics over the time scales of days, weeks, and months. At the level of user
behavior, we employ our recently developed hedonometric analysis methods to
investigate patterns of sentiment expression. We find users' average happiness
scores to be positively and significantly correlated with those of users one,
two, and three links away. We strengthen our analysis by proposing and using a
null model to test the effect of network topology on the assortativity of
happiness. We also find evidence that more well connected users write happier
status updates, with a transition occurring around Dunbar's number. More
generally, our work provides evidence of a social sub-network structure within
Twitter and raises several methodological points of interest with regard to
social network reconstructions.Comment: 22 pages, 21 figures, 5 tables, In press at the Journal of
Computational Scienc
Measuring the happiness of large-scale written expression: Songs, blogs, and presidents
The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960s to the mid 1990s while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the Earth\u27s equator. © 2009 The Author(s)
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