Abstract: Our usage of language is not solely reliant on cognition but is arguably determined by myriad external factors leading to a global variability of linguistic patterns. This issue, which lies at the core of sociolinguistics and is backed by many small-scale studies on face-to-face communication, is addressed here by constructing a dataset combining the largest French Twitter corpus to date with detailed socioeconomic maps obtained from national census in France. We show how key linguistic variables measured in individual Twitter streams depend on factors like socioeconomic status, location, time, and the social network of individuals. We found that (i) people of higher socioeconomic status, active to a greater degree during the daytime, use a more standard language; (ii) the southern part of the country is more prone to use more standard language than the northern one, while locally the used variety or dialect is determined by the spatial distribution of socioeconomic status; and (iii) individuals connected in the social network are closer linguistically than disconnected ones, even after the effects of status homophily have been removed. Our results inform sociolinguistic theory and may inspire novel learning methods for the inference of socioeconomic status of people from the way they tweet.

Introduction: Communication is highly variable and this variability contributes to language change and fulfills social functions. Analyzing and modeling data from social media allows the high-resolution and long-term follow-up of large samples of speakers, whose social links and utterances are automatically collected. This empirical basis and long-standing collaboration between computer and social scientists could dramatically extend our understanding of the links between language variation, language change, and society. Languages and communication systems of several animal species vary in time, geographical space, and along social dimensions. Varieties are shared by individuals frequenting the same space or belonging to the same group. The use of vocal variants is flexible. It changes with the context and the communication partner and functions as "social passwords" indicating which individual is a member of the local group BIBREF0 . Similar patterns can be found in human languages if one considers them as evolving and dynamical systems that are made of several social or regional varieties, overlapping or nested into each other. Their emergence and evolution result from their internal dynamics, contact with each other, and link formation within the social organization, which itself is evolving, composite and multi-layered BIBREF1 , BIBREF2 . The strong tendency of communication systems to vary, diversify and evolve seems to contradict their basic function: allowing mutual intelligibility within large communities over time. Language variation is not counter adaptive. Rather, subtle differences in the way others speak provide critical cues helping children and adults to organize the social world BIBREF3 . Linguistic variability contributes to the construction of social identity, definition of boundaries between social groups and the production of social norms and hierarchies. Sociolinguistics has traditionally carried out research on the quantitative analysis of the so-called linguistic variables, i.e. points of the linguistic system which enable speakers to say the same thing in different ways, with these variants being "identical in reference or truth value, but opposed in their social [...] significance" BIBREF4 . Such variables have been described in many languages: variable pronunciation of -ing as [in] instead of [iŋ] in English (playing pronounced playin'); optional realization of the first part of the French negation (je (ne) fume pas, "I do not smoke"); optional realization of the plural ending of verb in Brazilian Portuguese (eles disse(ram), "they said"). For decades, sociolinguistic studies have showed that hearing certain variants triggers social stereotypes BIBREF5 . The so-called standard variants (e.g. [iŋ], realization of negative ne and plural -ram) are associated with social prestige, high education, professional ambition and effectiveness. They are more often produced in more formal situation. Non-standard variants are linked to social skills, solidarity and loyalty towards the local group, and they are produced more frequently in less formal situation. It is therefore reasonable to say that the sociolinguistic task can benefit from the rapid development of computational social science BIBREF6 : the similarity of the online communication and face-to-face interaction BIBREF7 ensures the validity of the comparison with previous works. In this context, the nascent field of computational sociolinguistics found the digital counterparts of the sociolinguistic patterns already observed in spoken interaction. However a closer collaboration between computer scientists and sociolinguists is needed to meet the challenges facing the field BIBREF8 : The present work meets most of these challenges. It constructs the largest dataset of French tweets enriched with census sociodemographic information existent to date to the best of our knowledge. From this dataset, we observed variation of two grammatical cues and an index of vocabulary size in users located in France. We study how the linguistic cues correlated with three features reflective of the socioeconomic status of the users, their most representative location and their daily periods of activity on Twitter. We also observed whether connected people are more linguistically alike than disconnected ones. Multivariate analysis shows strong correlations between linguistic cues and socioeconomic status as well as a broad spatial pattern never observed before, with more standard language variants and lexical diversity in the southern part of the country. Moreover, we found an unexpected daily cyclic evolution of the frequency of standard variants. Further analysis revealed that the observed cycle arose from the ever changing average economic status of the population of users present in Twitter through the day. Finally, we were able to establish that linguistic similarity between connected people does arises partially but not uniquely due to status homophily (users with similar socioeconomic status are linguistically similar and tend to connect). Its emergence is also due to other effects potentially including other types of homophilic correlations or influence disseminated over links of the social network. Beyond we verify the presence of status homophily in the Twitter social network our results may inform novel methods to infer socioeconomic status of people from the way they use language. Furthermore, our work, rooted within the web content analysis line of research BIBREF9 , extends the usual focus on aggregated textual features (like document frequency metrics or embedding methods) to specific linguistic markers, thus enabling sociolinguistics knowledge to inform the data collection process.

Question: How do they combine the socioeconomic maps with Twitter data? 
Answer: Match geolocation data for Twitter users with patches from INSEE socioeconomic maps.