brainscopycut.tex

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\title{The semantic drift of quotations in blogspace: a case study in short-term cultural evolution}
\shorttitle{The semantic drift of quotations in blogspace}
\date{}
\twoauthors{Sébastien Lerique}{Camille Roth}
\twoaffiliations{Centre d'Analyse et de Mathématique Sociales, UMR 8557 CNRS/EHESS\\190 av. de France, F-75013 Paris\\and\\Centre Marc Bloch Berlin e.V., UMIFRE 14 CNRS/MAEDI, Friedrichstr. 191, D-10117 Berlin}{Sciences Po, médialab, Paris, France\\and\\Centre Marc Bloch Berlin e.V., UMIFRE 14 CNRS/MAEDI, Friedrichstr. 191, D-10117 Berlin}
\abstract{
\newtext{We present an empirical case study which connects psycholinguistics with the field of cultural evolution, in order to test for the existence of cultural attractors in the evolution of quotations.
Such attractors have been proposed as a useful concept for understanding cultural evolution in relation with individual cognition, but their existence has been hard to test.
We focus on the transformation of quotations when they are copied from blog to blog or media website:
by coding words with a number of well-studied lexical features, we show that the way words are substituted in quotations is consistent (1) with the hypothesis of cultural attractors, and (2) with known effects of the word features.
In particular, words known to be harder to recall in lists have a higher tendency to be substituted, and words easier to recall are produced instead.
Our results support the hypothesis that cultural attractors can result from the combination of individual cognitive biases in the interpretation and reproduction of representations.}
}
\authornote{\emph{Keywords:} word recall; recollection bias; semantic network; cultural evolution; cultural attraction; data mining; big data; psycholinguistics.\\\emph{Corresponding author:} Sébastien Lerique, CAMS, EHESS, 190 av. de France, F-75013 Paris. Email: sebastien.lerique@normalesup.org.}

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\subsection*{Acknowledgements}

We are warmly grateful to Ana Sofia Morais for her precious feedback and advice on this research, and to Telmo Menezes, Jean-Philippe Cointet, Jean-Pierre Nadal, Sharon Peperkamp, \newtext{Nicolas Claidière} and Nicolas Baumard for useful suggestions and comments.

{\new This work has also been partially supported by the French National Agency of Research (ANR) through the grant Algopol (ANR-12-CORD-0018).}

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\subsection*{Software colophon}

Finally, this paper was developed using Python's scientific computing ecosystem~\citep{millman_python_2011}.
In particular, we directly used NumPy and SciPy~\citep{walt_numpy_2011}, Matplotlib~\citep{hunter_matplotlib:_2007}, Pandas~\citep{mckinney_data_2010}, scikit-learn~\citep{pedregosa_scikit-learn:_2011}, NetworkX~\citep{hagberg_exploring_2008}, NLTK~\citep{bird_nltk_2009}, IPython~\citep{perez_ipython:_2007}, and many other libraries from the Python ecosystem.
The software and analyses written for the paper are documented and published under a Free Software license.
They can be found at \url{https://github.com/wehlutyk/brainscopypaste}.

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