Integrating Computer Prediction Methods in Social Science: A Comment on Hofman et al. (2021)

Machine learning and other computer-driven prediction models are one of the fastest growing trends in computational social science. These methods and approaches were developed in computer science and with different goals and epistemologies than those in social science. The most obvious difference being a focus on prediction versus explanation. Predictive modeling offers great potential for improving research and theory development, but its adoption poses some challenges and creates new problems. For this reason, Hofman et al. published recommendations for more effective integration of predictive modeling into social science. In this communication, I review their recommendations and expand on some additional concerns related to current practices and whether prediction can effectively serve the goals of most social scientists. Overall, I argue they provide a sound set of guidelines and a classification scheme that will serve those of us working in computational social science.

Automated summary

Machine learning and other computer-driven prediction models are increasingly popular in computational social science. These methods, developed in computer science, have different goals and epistemologies than social science, primarily focusing on prediction rather than explanation. While predictive modeling offers potential for improving research and theory development, it also presents challenges. To address these challenges, Hofman et al. provide recommendations and highlight additional concerns related to current practices and the effectiveness of prediction for social scientists.