3 research outputs found
The Simmel effect and babies names
Simulations of the Simmel effect are performed for agents in a scale-free
social network. The social hierarchy of an agent is determined by the degree of
her node. Particular features, once selected by a highly connected agent,
became common in lower class but soon fall out of fashion and extinct.
Numerical results reflect the dynamics of frequency of American babies names in
1880-2011.Comment: 11 pages, 7 figure
Cross correlations of the American baby names
The quantitative description of cultural evolution is a challenging task. The
most difficult part of the problem is probably to find the appropriate
measurable quantities that can make more quantitative such evasive concepts as,
for example, dynamics of cultural movements, behavior patterns and traditions
of the people. A strategy to tackle this issue is to observe particular
features of human activities, i.e. cultural traits, such as names given to
newborns. We study the names of babies born in the United States of America
from 1910 to 2012. Our analysis shows that groups of different correlated
states naturally emerge in different epochs, and we are able to follow and
decrypt their evolution. While these groups of states are stable across many
decades, a sudden reorganization occurs in the last part of the twentieth
century. We think that this kind of quantitative analysis can be possibly
extended to other cultural traits: although databases covering more than one
century (as the one we used) are rare, the cultural evolution on shorter time
scales can be studied thanks to the fact that many human activities are usually
recorded in the present digital era.Comment: submitted for consideration to PNA
The application of growth curve modeling for the analysis of diachronic corpora
This paper introduces growth curve modeling for the analysis of language change in corpus linguistics. In addition to describing growth curve modeling, which is a regression-based method for studying the dynamics of a set of variables measured over time, we demonstrate the technique through an analysis of the relative frequencies of words that are increasing or decreasing over time in a multi-billion word diachronic corpus of Twitter. This analysis finds that increasing words tend to follow a trajectory similar to the s-curve of language change, whereas decreasing words tend to follow a decelerated trajectory, thereby showing how growth curve modeling can be used to uncover and describe underlying patterns of language change in diachronic corpora