LAUSR

ABSTRACT In this theoretical piece, we discuss the limitations of using purely computational techniques to study big language data produced by people online. Instead, we advocate for mixed-method approaches that are able to more critically evaluate and consider the individual and social impact of this data. We propose one approach that combines qualitative, traditional quantitative, and computational methods for the study of language and text. Such approaches leverage the speed and expediency of computational tools while also highlighting the value of qualitative methods in critically assessing the outcome of computational results. In addition to this, we highlight two considerations for communication scholars utilizing big data: (1) the need to consider more language variations and (2) the importance of self-reflexivity when conducting big language data research. We conclude with additional recommendations for researchers seeking to adopt this framework in the context of their own research.