We have discussed how computational social science is seen as a field where a multidisciplinary approach and skills is required. Current commentators, such as Grimmer (2015), highlight how the knowledge of both computer scientists and social scientists are needed to work on problems together. Similarly, (Wallach, 2018) states that computational social science ought not to be just data science with social data. Rather, she claims that social scientists need to work with computer scientists in this emerging research domain. These examples illustrate how the multidisciplinary venture of computational social science can, with contributions from computer scientists, physicists, statisticians and social scientists, merge.
However, different approaches seek their own type of refocus on social science. Table 1.1 characterises the different approaches discussed about computational social science and then suggests main aims for these approaches. It is a blunt description of reality, and there must be a lot of work taking place in these boundaries. However, these characterisations demonstrate how different types of scientific contributions may be more suitable for communities. It is critical to understand these differences to be able to better situate oneself within this broader body of scholarship; therefore, we have started the book through this discussion. Furthermore, in this type of field, you may encounter people with strongly different ideas about what the aim of computational social science is and what type of contributions are expected. Based on my own experiences, these challenges can even become emotionally charged. Differences in core assumptions may be difficult to discuss even among academics. However, for computational social science to succeed, everyone must be able to cooperate across these types of collaborations. We will return to the opportunities and problems of multidisciplinary collaboration in Chapter 11. For now, we focus only on two conceptual frameworks that may help to understand collaboration when occurring across such potential divisions.
First, a trading zone helps scholars from different communities to exchange their ideas successfully (Galison, 1999). The main idea of the trading zone is that only smaller `bytes' of information exchange across the community and not the fully developed and complex theoretical jargon. For example, Galison (1999) observes that cross-disciplinary communication may require more simplified language to emerge to support collaboration, where different disciplinaries might disagree with the meaning of a concept and its background, but the terms help the collaboration to emerge. In the context of social scientists, we have identified approaches that may need collaboration across their own perspectives. Therefore, developing collaboration may require relaxing some assumptions and terms used within this community and allowing other communities to provide their own meanings to these terms. However, as Galison (1999) observes, building successful collaborations may require time and deep integration.
Second, scholarship takes place within a paradigm. A paradigm constitutes an accepted way of conducting studies and synthesising knowledge. A paradigm is shared among a substantial portion of researchers in a discipline (Kuhn, 2012). In computational social sciences, we may see scholars from different paradigms. Their paradigm may be accepted within their own subfield. Thus, acknowledging and seeing how the existing paradigms shape one's own understanding of what constitutes proper science is critical. In the area of computational social science, Kitchin (2014) suggests that a new paradigm of research emerges where a hybrid approach, applying both deductive and inductive research logics, is valid. He seeks to highlight that in a data-driven research approach, traditional and deeply rooted approaches of conducting research (in social science) may no longer hold. Similarly, on a more generic level, Mills (2000) warns that `bureaucratic techniques inhabit social inquiry'. Therefore, a more relaxed and open approach may be required to understand that good research may occur outside one's paradigm as well as through traditions and even language, which may seem a bit weird.
That said, there are two critical aspects that computational social scientists will need to work in this ever-growing domain. First, sociological imagination is essential to be able to shift one's thinking from one perspective to another to reflect the interplay between an individual and society. This can be done by changing the theoretical lens, analysis level, literature applied or even research context (Mills, 2000). Changing the analysis level and examining interplays drive forward for creativity when thinking about what to research. Using this imagination, researchers think about different formulations of questions and how questions inform researchers about something that is taking place in our society, captures changes from an individual level to a wider context and â we hope â are of high relevance. Similarly, in computational social science, sociological imagination helps to develop suitable focus for research analysis, think about what is important in the study and how that reflects how societies and individuals behave. Technological imagination refers to the ability to use computational tools to capture the interesting questions. This means scholars should be able to understand possibilities of analysis techniques, maybe adapted from different paradigms, that may help in social inquiry. In computational social sciences, this means the ability to rethink and even develop existing tools used for research purposes to better support the chosen research question. The limits of technologies are almost endless, but the main challenge is how good the connection is between the technological approach and the research question. Sometimes repurposing tools that were not originally considered for research, like automated plagiarism detection tools, helps to understand interplay between press releases and media outcomes (Bail, 2012). Sometimes this requires a custom build of an analysis tool, like when examining where the imaginary used in nationalist social media groups emerges (Hokka and Nelimarkka, 2020). I am a strong supporter of methodological pluralism and allowing the research question to drive methodological choices. In this context, technological imagination highlights that there are rarely one-size-fits-all technical tools, but the interestingness emerges when one innovates how a question should be approached. Both forms of imagination are essential in computational social science. While sociological imagination serves in helping to choose the right research questions and creatively search for perspectives for it, technological imagination ensures that the computational approach is suitable to solve this research question and account for the perspective presented.