Understanding the paradigm shift to computational social science in the presence of big data
By Ray M. Chang, Robert J. Kauffman, YoungOk Kwon. Decision Support Systems, 2014
370 citations recorded by the source corpus.
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.
Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/b970f9c088beee99666a40374dd5ccb06eeda112
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
Citations in the corpus
- Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “Prior studies have already discussed the potential of big data for theory development via computational social science or massive experimentation (Agarwal & Dhar, 2014; Chang et al., 2014).”
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, background): “Big data have shown great potential in forecasting and better decision making [1]; though handling these data with conventional ways is inadequate [6], they are being continuously integrated in research [7] with novel approaches and methods.”