Michael Erard, a longtime LH favorite, has a good piece in Science on a paper by Shahar Ronen et al., “Links that speak: the global language network and its association with global fame“:
The study was spurred by a conversation about an untranslated book, says Shahar Ronen, a Microsoft program manager whose Massachusetts Institute of Technology (MIT) master’s thesis formed the basis of the new work. A bilingual Hebrew-English speaker from Israel, he told his MIT adviser, César Hidalgo (himself a Spanish-English speaker), about a book written in Hebrew whose translation into English he wasn’t yet aware of. “I was able to bridge a certain culture gap because I was multilingual,” Ronen says. He began thinking about how to create worldwide maps of how multilingual people transmit information and ideas.
Ronen and co-authors from MIT, Harvard University, Northeastern University, and Aix-Marseille University tackled the problem by describing three global language networks based on bilingual tweeters, book translations, and multilingual Wikipedia edits. The book translation network maps how many books are translated into other languages. For example, the Hebrew book, translated from Hebrew into English and German, would be represented in lines pointing from a node of Hebrew to nodes of English and German. That network is based on 2.2 million translations of printed books published in more than 1000 languages. As in all of the networks, the thickness of the lines represents the number of connections between nodes. For tweets, the researchers used 550 million tweets by 17 million users in 73 languages. In that network, if a user tweets in, say, Hindi as well as in English, the two languages are connected. To build the Wikipedia network, the researchers tracked edits in up to five languages done by editors, carefully excluding bots.
In all three networks, English has the most transmissions to and from other languages and is the most central hub, the team reports online today in the Proceedings of the National Academy of Sciences. But the maps also reveal “a halo of intermediate hubs,” according to the paper, such as French, German, and Russian, which serve the same function at a different scale.
In contrast, some languages with large populations of speakers, such as Mandarin, Hindi, and Arabic, are relatively isolated in these networks. This means that fewer communications in those languages reach speakers of other languages. Meanwhile, a language like Dutch—spoken by 27 million people—can be a disproportionately large conduit, compared with a language like Arabic, which has a whopping 530 million native and second-language speakers. This is because the Dutch are very multilingual and very online. […]