Mapping Brussels’ linguistic diversity

The interactive maps and tables on this page use anonymised, aggregated data from Meta platforms (Facebook and Instagram) to estimate the frequency with which different languages are used in each of the 19 communes of the Brussels-Capital Region. The data reflect where people are located when using Meta platforms and which languages they are believed to be using, based on the language of the content they follow, of their app settings, and of the posts they engage with.

The estimates based on these data do not represent language proficiency, home language, origin, or the size of linguistic communities, but they do provide plausible estimates of language exposure and preferences. Three biases must be born in mind, however. Firstly, the use of some languages — particularly English — may be overstated due to its being a default fallback language in device settings and to the inevitable exposure to English-language content on social media. Secondly, the numbers will also include people who don't reside in Brussels (such as daily commuters and visitors), which explains why estimates can outstrip population size. Thirdly, the estimates will also be skewed towards Meta's userbase — although widespread adoption of the internet and of Meta's platforms are so widespread that it is reasonable to assume the numbers cover a large segment of the population.

Despite these limitations, these estimates constitute an informative and novel empirical supplement to other sources such as census and survey data - including the remarkable Taalbarometer - whose high fielding costs often prevent data as granular as this. A number of studies have made similar use of this data:

• Leveraging Digital and Computational Demography for Policy Insights. Kashyap, R., Zagheni, E. (2023). In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of Computational Social Science for Policy. Springer, Cham.
• Mapping socioeconomic indicators using social media advertising data. Fatehkia, M., Tingzon, I., Orden, A., Sy, S., Garcia-Herranz, M., & Weber, I. (2020). EPJ Data Science, 9, 22.
• Is Facebook's advertising data accurate enough for use in social science research? Insights from a cross-national online survey. Grow, A., Perrotta, D., Del Fava, E., Cimentada, J., Rampazzo, F., Gil-Clavel, S., Zagheni, E., Flores, R. D., Ventura, I., & Weber, I. (2022). Journal of the Royal Statistical Society: Series A (Statistics in Society), 185(S2), S343–S363.
• Where have Ukrainian refugees gone? Identifying potential settlement areas across European regions integrating digital and traditional geographic data. Miguel González-Leonardo, Ruth Neville, Sofía Gil-Clavel, Francisco Rowe (2024). Population, Space and Place,Volume 30, Issue 8, November 2024, e2790
• Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide. Rama, D., Mejova, Y., Tizzoni, M., Kalimeri, K., & Weber, I. (2020). arXiv preprint arXiv:2002.11645.
• The potential of Facebook advertising data for understanding flows of people from Ukraine to the European Union.Minora, U., Bosco, C., Iacus, S. M., Grubanov-Boskovic, S., Sermi, F., & Spyratos, S. (2022). EPJ Data Science, 11, 59.
• Monitoring of Venezuelan mass migration with Facebook’s advertising platform Palotti, J., Adler, N., Morales-Guzmán, A., Villaveces, J., Sekara, V., García Herranz, M., & Weber, I. (2020). PLOS ONE, 15(10), e0240821.