A Matter of Perspective

Abstract

Abstract We are living in a golden age for social science—a time when researchers have both unprecedented access to human data and the computational power to interrogate those data. Innovation has proceeded with remarkable speed; new methods regularly deliver novel insights with increasingly improved accuracy. These data and tools have greatly impacted the social sciences by allowing researchers to ask new questions in meaningful, new ways and to revisit classic questions with renewed rigor. While computational capacity is no longer the insurmountable barrier it once was, the opportunity presented by this methodological innovation underscores the urgent need to address another long-standing limitation of scientific advancement: the relative homogeneity of the research community. Methodological pluralism—diversity of both methods and methodologists—is essential for ensuring the collective creativity necessary to fully leverage the tools and data available to researchers. How we ask and answer questions matters. How we conceive of questions, what data and tools we bring to those questions, and how we interpret our findings are all intimately interconnected with researcher perspectives and identities. If social science ultimately aims to understand the human experience, it cannot do so without full consideration for the diversity of that experience. This paper therefore details recent innovations in computational social science and machine learning, highlighting the connections between researcher perspectives, methodological choices, and data interpretation. With a particular focus on text and network methods, we illustrate the role of researcher choice in shaping scientific advancement and demonstrate the need for human interpretation of algorithmic output. By interrogating the role of human researchers in scientific discovery we showcase the value of methodological pluralism and argue that diversity is an essential and necessary condition for scientific advancement.