Understanding the paradigm shift to computational social science in the presence of big data

article i nfo Available online xxxx The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data col- lection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts in- clude social networks and blogs, political discourse, corporate announcements, digital journalism, mobile tele- phony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and the new capabilities that researchers have to conduct re- search that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientifi cp aradigm shift toward computational social science. The new thinking related to empirical regularities analysis, experimen- tal design, and longitudinal empirical research further suggests that these approaches can be tailored for rapid acquisition of big data sets. This will allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, and illustrate our perspective with comparisons between earlier and current re- search inquiry. We argue against the assertion that theory no longer matters and offer some new research directions.