Statistical analysis of word flow among five Indo-European languages
This provides a data-driven approach to study cultural influence through language for linguists and historians, though it is incremental in applying existing statistical methods to new linguistic data.
The researchers analyzed word flow among five Indo-European languages using the Google Books Ngram dataset, quantifying 'migrant words' (loanwords with unchanged spelling) across decades and finding they cluster in semantic fields linked to historical events. They also proposed a measure of migrant word usage as a proxy for cultural influence.
A recent increase in data availability has allowed the possibility to perform different statistical linguistic studies. Here we use the Google Books Ngram dataset to analyze word flow among English, French, German, Italian, and Spanish. We study what we define as ``migrant words'', a type of loanwords that do not change their spelling. We quantify migrant words from one language to another for different decades, and notice that most migrant words can be aggregated in semantic fields and associated to historic events. We also study the statistical properties of accumulated migrant words and their rank dynamics. We propose a measure of use of migrant words that could be used as a proxy of cultural influence. Our methodology is not exempt of caveats, but our results are encouraging to promote further studies in this direction.