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The paper provides an outline of the scope for synergy between computational linguistic analysis and population studies. It first reviews where population studies stand in terms of using social media data. Demographers are entering the realm of big data in force. But, this paper argues, population studies have much to gain from computational linguistic analysis, especially in terms of explaining the drivers behind population processes. The paper gives two examples of how the method can be applied, and concludes with a fundamental caveat. Yes, computational linguistic analysis provides a possible key for integrating micro theory into any demographic analysis of social media data. But results may be of little value in as much as knowledge about fundamental sample characteristics are unknown. 1 The incomplete data revolution in demography Demography is the study of population. Traditionally, demography is concerned with measuring and estimating population change by births, deaths and migration. Demography is rooted in quantitative methods, with data at its heart. As the field moved through different epochs of data availability, in demography data have always been "big" (Billari and Zagheni, 2017). Starting with the exercise of mapping macro-level trends through population level parameters, based largely on census and administrative records, the field became more theory driven as individual data became available. It is fair to say that with the explosion in available survey data, a revolution in demographic studies took place. Rather than simply describing demographic patterns, today demographers are equally concerned in understanding both the drivers and the consequences of demographic processes. In doing so, demographers have assembled an enormously rich set of data for explaining not only population processes, but also the motivational and behavioral drivers behind these processes. However, data generated by surveys may have peaked. As survey and polling agencies struggle with increasing costs and declining survey response rates, statistic producers are increasingly looking towards big data. Still, given their quantitative pedigree, demographers are perhaps better placed than most other social scientists to take on the challenge of the new big data revolution. Demographers are, in fact, already using big data to describe demographic processes, including data derived from social media. But there are challenges. Big data is messy and unstructured, and this is a considerable challenge for a scientific field acutely concerned with representativeness and unbiased estimation. Social media provides a promising avenue, however, as demographers are interested not only in describing population processes, but also in the motivations that individuals have for their behavior, which, ultimately, generates observed population processes. For demographers in search of the determinants and consequences of demographic behavior, the linguistic analysis of social media texts can offer a precious and rich new source. Caution – as this paper highlights – is necessary, since its non-representativeness and partiality makes it problematic in social-science terms. The rapid emergence of big data from social media outpaced social scientists’ capacity for using and analyzing them. That having been said,

The Potential of the Computational Linguistic Analysis of Social Media for Population Studies