This paper examines the challenges and opportunities that ''big data'' poses to scholars advancing research frontiers in the social sciences. It examines the strengths and weaknesses of machine-based and human-centric approaches to information extraction and argues for use of a hybrid approach, one that employs tools developed by data scientists to leverage the relative strengths of both machines and humans. The notion of a progressive, supervised-learning approach is developed and illustrated using the Social, Political and Economic Event Database (SPEED) project''s Societal Stability Protocol (SSP). The SSP generates rich event data on civil strife and illustrates the advantages of employing a supervised-learning approach in contrast to conventional approaches for generating civil strife data. We show that conventional event-count approaches miss a great deal of within-category variance (e.g., number of demonstrators, types of weapons used, number of people killed or injured). We also show that conventional efforts to categorize longer periods of civil war or societal instability have been systematically mis-specified. To demonstrate the capacity of rich event data to open new research frontiers, SSP data on event intensities and origins are used to trace the changing role of political, class-based and socio-cultural factors in generating civil strife over the post WWII era.