Learning about the world with computational social science
2.5. Learning about the world with computational social science¶
Computational social science offers us new ways to learn about the world. New data sources emerge, either made available through digitization or bursting into existences through digitalization. New computational methods become available to social scientists. And people coming from different interdisciplinary backgrounds develop new interests in social phenomena and human behavior. This makes CSS into a promising interdisciplinary area. A space of crossroads for people who share interests in social phenomena and human behavior and where people from different scientific backgrounds meet.
Thus it comes as no surprise that CSS comes in many names and has many relations: Some think of it in topical subfields, such as Computational Communication Science (CCS), data science, or sociophysics. Others think of it not as a field but primarily see it through associated methods, such as agent based modeling, network analysis, or computational text analysis. For others still, it is simply a subfield of applied computer science concerned with social systems and phenomena. Each of these perspectives comes with specific insights, strengths, and contributions. Too many in fact to be given appropriate space in this and the preceding episodes.
Still, there are shared concerns among researchers "who develop and test theories or provide systematic descriptions of human, organizational, and institutional behavior through the use of computational methods and practices" [Theocharis and Jungherr, 2021], p. 4. Those include facing the conceptual challenges of translating social science theories and interests into computational concepts and operationalizations, connecting new data sources with established theories or interests while remaining open to new phenomena and behavioral patterns, and the integration of practices and workflows from different disciplines in order to capitalize on the new opportunities emerging from interdisciplinary efforts. Every scholar and every interdisciplinary team across the multitude of CSS subfields face these concerns and challenges to different degrees. There is value in remaining aware of the shared roots, concerns, and challenges of computational social science instead of splintering too early into subfields driven by topical interests or methods. This splintering would risk diluting the collective attention and effort to discussing and working through the challenges of CSS.
Computational social science in all its facettes offers scientists new ways to learn about the world and the impact of digital media on politics and society in particular. True, the emotional response to the incessant interdisciplinary efforts in shaping, contesting, and improving goals, methods and practices of CSS can suddenly shift from being exhilarating to exhausting. For anyone, working in CSS means moving out of their comfort zone of field-specific theories, methods, practices, and workflows. Truly lived scientific interdisciplinarity is challenging. But at the same time also exciting and promising. As in every new area of research, work in CSS is characterized by uncertainties. At the same time, however, it is an area that, precisely because of its freshness and the open questions associated with it, brings unbelievable dynamics thematically, theoretically and methodologically. This makes it, without question, one of the most exciting and rewarding areas of social science today.