Using Big Data Methods to Identify Conceptual Frameworks

AbstractConceptual frameworks such as religion or politics may play a pervasive role in people’s interpretation of experience, but the empirical evidence for such effects is limited. To the extent that conceptual frameworks are real, they should have a pervasive impact on how people talk about the world. Such an influence may be detected in people’s everyday language. In a series of studies, text from the social media platform Reddit was used to train machine learning classifiers to identify people’s association with a particular religion or mental disorder. Impressively, classifiers trained on text focusing on religion and mental disorders could be used to identify people’s association with a particular religion or mental disorder even when the text was not explicitly about these topics, such as when it was about buying a car or playing tennis. Not only could the classifiers predict people’s religion or mental illness in the present, they could also do so prospectively, indicating that people’s everyday language gives away information about the kinds of conceptual frameworks they may hold in the future. An analysis of the features learned by the classifier suggested that they learned features with high face validity for the underlying conceptual framework. Together, the results provide evidence for the existence of conceptual frameworks by virtue of the imprint they leave across a wide range of language contexts.


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