Computational Social Science and Big Data: A Quick SWOT Analysis

Abstract

Do computational social science and Big Data constitute a methodological revolution of the complex, data-intensive sciences? his question is approached by means of a quick analysis of strengths, weaknesses, opportunities and threats (SWOT analysis) of the two approaches� It is concluded that computational social science and Big Data do mark an important methodological improvement but should probably not qualiied as "scientiic revolution" or "paradigm change"� From the SWOT analysis it also follows that further research is necessary for a coherent development of computational social science and Big Data, in particular with respect to the ethics of privacy; balancing the low explanatory power of computational models; developing an epistemological position between naïve realism and radical constructivism; integrating computer science and social science� 131 heodor Leiber is Associate Professor of Philosophy at University of Augsburg (Germany)� He received doctorates in theoretical physics and philosophy� His main areas of interest are philosophy of science and technology, epistemology and ethics of nature� Leiber is also a higher education researcher focusing on models of teaching and learning, governance and impact analysis of quality management in higher education�