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Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions.

Political scientists, pundits, and citizens worry that America is entering a new period of violent partisan conflict. Provocative survey data show that a large share of Americans (between 8% and 40%) support politically motivated violence. Yet, despite media attention, political violence is rare, amounting to a little more than 1% of violent hate crimes in the United States. We reconcile these seemingly conflicting facts with four large survey experiments (n = 4,904), demonstrating that self-reported attitudes on political violence are biased upward because of respondent disengagement and survey questions that allow multiple interpretations of political violence. Addressing question wording and respondent disengagement, we find that the median of existing estimates of support for partisan violence is nearly 6 times larger than the median of our estimates (18.5% versus 2.9%). Critically, we show the prior estimates overstate support for political violence because of random responding by disengaged respondents. Respondent disengagement also inflates the relationship between support for violence and previously identified correlates by a factor of 4. Partial identification bounds imply that, under generous assumptions, support for violence among engaged and disengaged respondents is, at most, 6.86%. Finally, nearly all respondents support criminally charging suspects who commit acts of political violence. These findings suggest that, although recent acts of political violence dominate the news, they do not portend a new era of violent conflict.