Computational social sciences (CSS) stand out as fields based on using artificial intelligence and big-data methods, and quantitative social sciences stand out as fields based on applying statistical methods. Today, digitalization is an unstoppable megatrend. It characterizes the twenty-first century in many aspects of social, political, and cultural life. The deep structural changes that accompany digitalization have become both the subject matter and an instrument of the social sciences. Social scientists now use big data, machine learning, network analysis in computational methods, and (quasi) experimental methods in quantitative methods to analyze the social world. These novel methods can be applied e.g. to distill measures from new data sources (texts and images), to characterize population heterogeneity, to improve causal inference, and to offer predictions in aiding policy decisions and theory development.[1]^,[2]^,[3]^,[4]^,[5]^,[6]

The social sciences research field of “Digital Societies” is becoming increasingly important for the public to deal with the challenges that modern societies are facing. This theme addresses the growing influence of digital technologies on trajectories of social and economic change and the implications for policymakers, civil society, and the private sector. Digital innovations are changing our society, economy, and industries with a speed like never before. Big data, the Internet of Things (IoT), mobile technologies offer unimaginable opportunities and improvements of our lives to many aspects of life including health, energy, agriculture, transportation, and public administration.

Despite this development in the state-of-the art, there are significant disparities in terms of research and innovation performance between Turkey and the better performing European Member states as well as North American countries in computational and quantitative social sciences (CSS and QSS) together with the field of Digital Societies. The visibility of CSS  has significantly grown since 2008 in Europe and the United States while the rise in Turkey is far behind the global trend.

In addition, there is currently no graduate program on CSS at Turkish universities. Moreover, the discrepancy between Turkey and other European countries to attract funding for research in social sciences is still low. A similar problem persists in the field of QSS. This constitutes a barrier for Turkey to improve its research and development potential. Therefore, social science researchers in Turkey are in high need of state-of-the-art computational and quantitative methods that would provide them with more powerful tools than traditional scientific methods.

[1] Molina, M.- Garip, F. Machine Learning for Sociology 2019.

[2] Athey, S. Beyond prediction: using big data for policy problems, 2017.

[3] Berk, R.A. et al. Forecasting domestic violence: a machine learning approach to help  inform arraignment decisions, 2016.

[4] Blumenstock, J. et al. Predicting poverty and wealth from mobile phone metadata, 2015.

[5] Grimmer, J.- Stewart, B.M.,Text as data: the promise and pitfalls of automatic content analysis methods  for political texts, 2013.

[6] Jordan, M.I., Mitchell, T.M., Machine learning: trends, perspectives, and prospects, 2015.