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Cited by 294Linguistic and Sociocultural StudiesHuman Mobility and Location-Based AnalysisDigital Humanities and Scholarship

The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.

The Twitter of Babel: Mapping World Languages through Microblogging Platforms

Open access
Mocanu, Delia, Baronchelli, Andrea, Perra, Nicola, Gonçalves, Bruno, Zhang, Qian, Vespignani, Alessandro·2013·PLoS ONE
DOI 10.1371/journal.pone.0061981PMC PMC3630228
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Abstract

Large scale analysis and statistics of socio-technical systems that just a few short years ago would have required the use of consistent economic and human resources can nowadays be conveniently performed by mining the enormous amount of digital data produced by human activities. Although a characterization of several aspects of our societies is emerging from the data revolution, a number of questions concerning the reliability and the biases inherent to the big data "proxies" of social life are still open. Here, we survey worldwide linguistic indicators and trends through the analysis of a large-scale dataset of microblogging posts. We show that available data allow for the study of language geography at scales ranging from country-level aggregation to specific city neighborhoods. The high resolution and coverage of the data allows us to investigate different indicators such as the linguistic homogeneity of different countries, the touristic seasonal patterns within countries and the geographical distribution of different languages in multilingual regions. This work highlights the potential of geolocalized studies of open data sources to improve current analysis and develop indicators for major social phenomena in specific communities.

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Figures

Figure 1 Multiscale view of the geolocated Twitter signal. The large number of geolocated Twitter traffic allows for a high resolution characterization of human behavior. A) Europe B) Italy C) Lazio region D) Rome. The squares highlight the zooming areas.
Figure 1 Multiscale view of the geolocated Twitter signal. The large number of geolocated Twitter traffic allows for a high resolution characterization of human behavior. A) Europe B) Italy C) Lazio region D) Rome. The squares highlight the zooming areas.CC BY · PubMed Central
Figure 2 Ranking of countries by users per capita. Ranking of countries as per average number of Twitter users over a population of individuals.
Figure 2 Ranking of countries by users per capita. Ranking of countries as per average number of Twitter users over a population of individuals.CC BY · PubMed Central
Figure 3 Users and GDP per capita. Correlation between country level Twitter penetration and GDP/capita. The adjusted R value of the fit is 0.56.
Figure 3 Users and GDP per capita. Correlation between country level Twitter penetration and GDP/capita. The adjusted R value of the fit is 0.56.CC BY · PubMed Central
Figure 4 User Activity. Probability density of user activity (number of daily tweets N) grouped by country (A) and language (B), and by country while considering English tweets exclusively (C). Different curves collapse naturally, without any functional rescaling, indicating the presence of a seemingly universal distribution of users activity, independent from cultural backgrounds. Countries in panel (A) and (C) are characterized by high Twitter penetration and represent different continents, while the languages in panel (B) are selected from those producing very strong signal. Dashed lines represent log-normal distributions , with and for (A), and (B), and and (C).
Figure 4 User Activity. Probability density of user activity (number of daily tweets N) grouped by country (A) and language (B), and by country while considering English tweets exclusively (C). Different curves collapse naturally, without any functional rescaling, indicating the presence of a seemingly universal distribution of users activity, independent from cultural backgrounds. Countries in panel (A) and (C) are characterized by high Twitter penetration and represent different continents, while the languages in panel (B) are selected from those producing very strong signal. Dashed lines represent log-normal distributions , with and for (A), and (B), and and (C).CC BY · PubMed Central
Figure 5 Languages by number of users. Languages ranked by total number of users. For clarity, only languages with more than users are shown.
Figure 5 Languages by number of users. Languages ranked by total number of users. For clarity, only languages with more than users are shown.CC BY · PubMed Central
Figure 6 Geographic distribution of languages around the world. A) Raw Twitter signal. Each color corresponds to a language. Densely populated areas are easily identified, while, as expected, languages are well separated among European countries. B) Dominant language usage. The color of each country indicates the fraction of users adopting the official language in tweets. Gray represent countries without statistically significant signal.
Figure 6 Geographic distribution of languages around the world. A) Raw Twitter signal. Each color corresponds to a language. Densely populated areas are easily identified, while, as expected, languages are well separated among European countries. B) Dominant language usage. The color of each country indicates the fraction of users adopting the official language in tweets. Gray represent countries without statistically significant signal.CC BY · PubMed Central
Figure 7 Language share of the most active countries. Language adopted by users coming from Top most active countries, ordered by number of English tweets.
Figure 7 Language share of the most active countries. Language adopted by users coming from Top most active countries, ordered by number of English tweets.CC BY · PubMed Central
Figure 8 Language polarization in Belgium and Catalonia, Spain. In each cell ( resolution) we compute the user-normalized ratio between the two languages being considered in each case. A) Belgium. B) Catalonia. The color bar is labeled according to the relative dominance of the language denoted by blue. In Belgium, English accounts for of the language share.
Figure 8 Language polarization in Belgium and Catalonia, Spain. In each cell ( resolution) we compute the user-normalized ratio between the two languages being considered in each case. A) Belgium. B) Catalonia. The color bar is labeled according to the relative dominance of the language denoted by blue. In Belgium, English accounts for of the language share.CC BY · PubMed Central
Figure 9 Language polarization in Montreal, QC, Canada. English and French are considered. In each cell () we compute the user-normalized ratio between English and French (excluding all other languages). Blue - English, Yellow - French. The color bar is labeled according to the relative dominance of English to French.
Figure 9 Language polarization in Montreal, QC, Canada. English and French are considered. In each cell () we compute the user-normalized ratio between English and French (excluding all other languages). Blue - English, Yellow - French. The color bar is labeled according to the relative dominance of English to French.CC BY · PubMed Central
Figure 10 Language polarization in New York City, NY, USA. The second language by district or municipality (in the case of New Jersey state) is shown. Blue - Spanish, Light Green - Korean, Fuchsia - Russian, Red - Portuguese, Yellow - Japanese, Pink - Dutch, Grey - Danish, Coral - Indonesian.
Figure 10 Language polarization in New York City, NY, USA. The second language by district or municipality (in the case of New Jersey state) is shown. Blue - Spanish, Light Green - Korean, Fuchsia - Russian, Red - Portuguese, Yellow - Japanese, Pink - Dutch, Grey - Danish, Coral - Indonesian.CC BY · PubMed Central
Figure 11 Monthly variations in Language use. Fraction of minority languages in specific countries as a function of the month. Increases in a specific language share indicate the presence of tourists visiting the country. Peaks are clearly visible during the local summer period.
Figure 11 Monthly variations in Language use. Fraction of minority languages in specific countries as a function of the month. Increases in a specific language share indicate the presence of tourists visiting the country. Peaks are clearly visible during the local summer period.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Mislove A, Lehmann S, Ahn YY, Onnela JP, Rosenquist JN (2011) Understanding the demographics of twitter users. In: In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.

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Metadata

Authors
Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A.
Affiliations
  • Northeastern University (US)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Vespignani, Alessandro
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2013-04-18
Volume
8
Issue
4
Pages
e61981
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0061981
PMC
PMC3630228
OpenAlex
W2044734026
Cited by
293
References
30
Citations by year
2026: 5 · 2025: 8 · 2024: 10 · 2023: 13 · 2022: 16 · 2021: 21 · 2020: 17 · 2019: 26 · 2018: 31 · 2017: 38 · 2016: 38 · 2015: 36 · 2014: 23 · 2013: 10
FWCI
151.78
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Greenwich Academic Literature Archive (University of Greenwich) (acceptedVersion)
  • City Research Online (City University London) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1101743)
  • Intelligence Advanced Research Projects Activity (D12PC00285)
Topics
  • Social Sciences › Linguistics and Language › Linguistic and Sociocultural Studies
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
Keywords
dialect geography, microblogging, Twitter, multilingual regions, linguistic homogeneity, language use patterns
MeSH terms
Humans, Language, Linguistics, Internet, Blogging, Data Mining, Social Media

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