The cultural environment: measuring culture with big data

The rise of the Internet, social media, and digitized historical archives has produced a colossal amount of text-based data in recent years. While computer scientists have produced powerful new tools for automated analyses of such "big data," they lack the theoretical direction necessary to extract meaning from them. Meanwhile, cultural sociologists have produced sophisticated theories of the social origins of meaning, but lack the methodological capacity to explore them beyond micro-levels of analysis. I propose a synthesis of these two fields that adjoins conventional qualitative methods and new techniques for automated analysis of large amounts of text in iterative fashion. First, I explain how automated text extraction methods may be used to map the contours of cultural environments. Second, I discuss the potential of automated text-classification methods to classify different types of culture such as frames, schema, or symbolic boundaries. Finally, I explain how these new tools can be combined with conventional qualitative methods to trace the evolution of such cultural elements over time. While my assessment of the integration of big data and cultural sociology is optimistic, my conclusion highlights several challenges in implementing this agenda. These include a lack of information about the social context in which texts are produced, the construction of reliable coding schemes that can be automated algorithmically, and the relatively high entry costs for cultural sociologists who wish to develop the technical expertise currently necessary to work with big data.