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global_language_network — Global language networks (2014)

Description

Networks of languages that are connected by multilingual speakers or by translations, from S. Ronen, B. Gonçalves, K. Z. Hu, A. Vespignani, S. Pinker and C. A. Hidalgo, built from three records of linguistic expression. Nodes are languages, and an edge connects two languages with a statistically significant number of shared users or translations, as follows:

  • books: book translations. A directed edge from language $i$ to language $j$ means that a significant number of books were translated from $i$ into $j$, according to UNESCO's Index Translationum, which lists 2.2 million translations of printed books published between 1979 and 2011.
  • twitter: an undirected edge connects two languages if users who tweet in one of them are significantly more likely to also tweet in the other, based on nearly 550 million tweets in 73 languages (language detected automatically) by more than 17 million users, collected from December 2011 to February 2012.
  • wikipedia: an undirected edge connects two languages if editors who edit an article in the Wikipedia edition of one language are significantly more likely to also edit an article in the edition of the other, based on 382 million edits in 238 language editions by 2.5 million editors, until the end of 2011 (excluding edits by bots).

For each pair of languages, the strength of the connection is measured by the $\phi$ correlation between the occurrence of the two languages, computed from the number $M_{ij}$ of common users (or translations from $i$ to $j$), and its significance by the corresponding $t$ statistic. The networks contain only the connections that involve at least six common users or translations (ten for Twitter in this data) and that are significant at $P < 0.01$ ($t > 2.58$), as in the visualizations of the paper; languages without any such connection are not included. The data are those of the interactive visualization of the networks by the authors' group (MIT Media Lab, now Center for Collective Learning).

The networks have the following properties:

  • Nodes: name, the ISO 639-3 code of the language; language, its name; family and primary_family, the language family at two levels (e.g. "Germanic" and "Indo-European"); speakers, the number of native and non-native speakers, in millions (NaN if not given, for 74 languages of the books network).
  • Edges: cooccurrences, the number $M_{ij}$ of common users (Twitter, Wikipedia) or of translations from the source to the target language (books); phi, the $\phi$ correlation; t_statistic, the $t$ statistic.1

  1. Description obtained in part from the ICON project. ↩

Tags
Informational Language Weighted Metadata
Citation
  • S. Ronen, B. Gonçalves, K. Z. Hu, A. Vespignani, S. Pinker, C. A. Hidalgo, "Links that speak: The global language network and its association with global fame." Proceedings of the National Academy of Sciences 111(52), E5616-E5622 (2014) 10.1073/pnas.1410931111 Sci-Hub
Upstream URL OK
https://github.com/CenterForCollectiveLearning/Global-language-network
https://web.archive.org/web/20150124132808/http://language.media.mit.edu/
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
books 247 545 2.21 13.52 9.69 8.26 -0.49 0.06 4 1.00 Directed Unipartite name language family primary_family speakers cooccurrences phi t_statistic 14 KiB 15 KiB 15 KiB 13 KiB
twitter 70 139 3.97 8.01 6.72 3.09 -0.43 0.10 3 1.00 Undirected Unipartite name language family primary_family speakers cooccurrences phi t_statistic 5 KiB 6 KiB 5 KiB 5 KiB
wikipedia 134 282 4.21 10.98 11.93 2.60 -0.46 0.11 4 1.00 Undirected Unipartite name language family primary_family speakers cooccurrences phi t_statistic 9 KiB 9 KiB 9 KiB 8 KiB