Computational social science heralds the age of interdisciplinary science

The fifth International Conference on Computational Social Science showed that we are entering an age of interdisciplinary scientific disciplines and science.

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The fifth International Conference on Computational Social Science (IC2S2) took place this past July in Amsterdam. While computational social science has existed for longer than IC2S2, it has gone through a revolution in the last ten years, due, in part, to the explosion of big data and scientists’ ability to analyze these large datasets. This version of computational social science, described ten years ago in a perspective by David Lazer and colleagues as “a computational social science… that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale (1),” included research on almost every topic related to human behavior. This, to me, is what makes computational social science unique. Instead of being a discipline centered around a single branch of knowledge (such as neuroscience is centered around the brain), it is a discipline characterized by the methods and data used to answer questions that previously would have fallen into disparate disciplines.

This is readily apparent from the range of topics covered by both the keynote and parallel oral presentations. It would be easiest to describe the range by providing a list, but since nobody likes to read lists, I will instead discuss some of the questions that computational scientists are answering.

For example, to date it has been hard to understand how rumors spread and how they can influence behavior. However, with the wealth of data provided by Twitter and other, similar online platforms, scientists can now describe how misinformation spreads and can directly observe how people respond: do they spread it further or stop it? What are the factors associated with spreading (or not spreading) this information? Does it have to do with the individual’s position in their own social network, in the greater social network, or the content of the message? Or a combination of these factors?

Similar techniques can be applied to understanding what drives inequalities - for example, between men and women, between the rich and the poor, or between countries – or the opposite, namely the factors associated with cooperation and prosocial behavior generally. Once you start to have a basic understanding of the factors that seem to drive inequality or cooperation, they can be applied to specific fields, for example, understanding political polarization in the US or understanding how to better predict individuals’ voting preferences.

Yet a whole other field of questions includes those about demography and health. Applying sophisticated computational modeling techniques to the large amounts of data on health has allowed researchers to understand patterns in fertility, disease, and the relationship between nutrition and various outcomes.

These example and many others led one of the local hosts of the meeting, Frank Takes, to provide a quantitative description of the interdisciplinarity of the participants at this conference.


He writes:

“Computational social science is often said to be highly interdisciplinary, as it typically attracts researchers from both the social sciences as well as computational science. To investigate this interdisciplinarity in more detail, we consider the scientific field migration activity of 480 registered participants of IC²S² 2019. During the conference registration process, people were asked to enter their original scientific discipline as well as their current discipline. From this data, a so-called scientific discipline migration network was extracted. This network is shown in the figure below. Each node (circle) in the network is a discipline. Each directed link (arrow) denotes movement from one discipline to the other. The width of a link is proportional to the link weight, i.e., the number of people moving between the disciplines that the arrow connects. The size of a node and its label correspond to the node's weighted indegree, denoting the number of people moving to that discipline. The color of a node is proportional to its weighted outdegree, representing how many people move away from that discipline.

We can observe a number of things from this network visualization. First, we note that computational social science itself is by far the discipline which is most often being moved towards from other fields, having the highest weighted indegree. The incoming links indeed originate from the various social sciences (sociology, psychology, political science and communication science) as well as computational sciences (mostly computer science and physics). Second, next to computational social science, we also observe other relatively new related fields, including complex systems, social network analysis and data science. Interestingly, the latter field, data science, which is sometimes said to be similar to computational social science, has a different position in this network. It indeed seems to draw from the same computational crowd as computational social science does, but barely anyone from the social sciences appears to indicate having moved there. This may be because data science is deemed to generic of a field for social scientists, and strengthens the claim that computational social science is truly about combining novel data-driven/computational techniques with substantial social science theory. We furthermore note that fields such as economics and management are present, hopefully indicating how computational social science is nowadays also attracting researchers who would otherwise label themselves as being active in the field of socio-economics. Last but not least, many people also indicate that computational social science is their original and current discipline, denoted by the self-loop. This is promising and to some extent rewarding, as the community has recently put a lot of effort in establishing computational social science as a field of its own, which apparently is taking shape already, at least based on the field's flagship conference's participants of today.”

To me, the fact that computational social science is becoming its own field signals that we are entering a true age of interdisciplinarity, as we are no longer categorizing disciplines according to the questions that they answer but rather according to the way in which the questions are approached. The result is a field categorized by methodological rigor, technical expertise, and a broad range of interests and knowledge. This, in my opinion, makes computational social science a model of what modern science should be: rooted in data and technical expertise, but unbounded in the questions it seeks to answer.

  

References:

1. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., ... & Jebara, T. (2009). Computational social science. Science, 323(5915), 721-723. 



Go to the profile of Mary Elizabeth Sutherland

Mary Elizabeth Sutherland

Senior Editor, Nature

behavioral sciences, including computational social science, social science, sociology, social psychology, cognitive psychology, social and affective neuroscience, cognitive neuroscience, neuroscience

1 Comments

Go to the profile of Chathika Gunaratne
Chathika Gunaratne 19 days ago

It's exciting that Computational Social Sciences is becoming its own discipline in it's own right and was great to witness the diversity of backgrounds of all the attendees of the IC2S2 2019 conference.

With it's coming of age, Computational Social Science does bring forth it's own set of problems, such as techniques to ensure scientific rigor that cross existing disciplinary boundaries, and relevant venues for peer-reviewed publication, given the huge diversity of methodologies being used by researchers in this field. I'm curious as to what the authors' views are on these issues going forward?