The Twitter of Babel: Mapping World Languages through Microblogging Platforms

By Delia Mocanu, Andrea Baronchelli, Bruno Gonçalves, Nicola Perra, Alessandro Vespignani. 2012

300 citations recorded by the source corpus.

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.

Subfields: Information Systems, Signal Processing

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “By mapping the use of languages across countries, Mocanu et al. (2013) observed seasonal variations in the linguistic composition of each country, as well as between geographical areas at diﬀerent granularity scales, even at the level of city neighborhoods.”
The Matthew effect in empirical data (2014, background): “The ecophysics of language change [136]—the application of models from statistical physics and theoretical ecology to the study of language dynamics—has since evolved into a beautiful and vibrant avenue of research [137–144].”
Science vs Conspiracy: Collective Narratives in the Age of Misinformation (2014, background): “A large body of literature addresses the study of social dynamics on socio-technical systems from social contagion up to social reinforcement [19–37].”
Twitter as Data (2018, methodology): “Twitter has also been used to predict crime in Chicago (Gerber, 2014) and map linguistic communities in New York City (Mocanu et al., 2013).”
Gender Inference of Twitter Users in Non-English Contexts (2013, background): “One recent paper characterized the relationship between language and geography (Mocanu et al., 2012).”
Geo-located Twitter as proxy for global mobility patterns (2013, background): “Leetaru et al. (2013) attempted to describe the geography of Twitter based on a one-month sample of global geolocated tweets, while Mocanu et al. (2013) described the global distribution of the different languages used while tweeting.”
Computational Sociolinguistics: A Survey (2016, background): “Mocanu et al. (2013) analyzed the geographic distribution of languages in multilingual regions and cities (such as New York and Montreal) using Twitter.”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, methodology): “To this end, we start our analysis with a Twitter data archive corresponding to all public tweets in English from March 1 to May 31, 20145—selected to avoid strong seasonal phenomena, typically observed in winter, summer and early fall [26, 68].”
Big Data for Development: A Review of Promises and Challenges (2016, unknown): “Traditional leaders of the global economy, such as Germany and Japan, are comparatively ill-equipped to satisfy domestic demand with internal sources.”