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Recently, numerous approaches have emerged in the social sciences to exploitthe opportunities made possible by the vast amounts of data generated by onlinesocial networks (OSNs). Having access to information about users on such ascale opens up a range of possibilities, all without the limitations associatedwith often slow and expensive paper-based polls. A question that remains to besatisfactorily addressed, however, is how demography is represented in the OSNcontent? Here, we study language use in the US using a corpus of text compiledfrom over half a billion geo-tagged messages from the online microbloggingplatform Twitter. Our intention is to reveal the most important spatialpatterns in language use in an unsupervised manner and relate them todemographics. Our approach is based on Latent Semantic Analysis (LSA) augmentedwith the Robust Principal Component Analysis (RPCA) methodology. We findspatially correlated patterns that can be interpreted based on the wordsassociated with them. The main language features can be related to slang use,urbanization, travel, religion and ethnicity, the patterns of which are shownto correlate plausibly with traditional census data. Our findings thus validatethe concept of demography being represented in OSN language use and show thatthe traits observed are inherently present in the word frequencies without anyprevious assumptions about the dataset. Thus, they could form the basis offurther research focusing on the evaluation of demographic data estimation fromother big data sources, or on the dynamical processes that result in thepatterns found here.

Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States