Computational Social Science
What is Computational Social Science?
Computational social science is an interdisciplinary field that leverages mathematical algorithms, advanced data analysis, and computational modeling to study and predict human behavior and social dynamics. By integrating techniques from computer science, statistics, and social sciences, computational social science offers powerful insights into complex social phenomena, enabling researchers to analyze large-scale data and simulate interactions in real time.
The Basic Idea
Imagine that you are a medical researcher, interested in predicting epidemic breakouts to prevent diseases from spreading. If you wait for an outbreak to be confirmed—meaning that an individual has received a positive test—then it’ll be very likely that the disease will have already started to make its way across the population.
What if there was a way to gauge who has the disease without having to wait for a positive test result? Well, there is a way—through computational social science!
Computational social science leverages computational methods and big data to study and predict human behavior in society. By applying tools from computer science to analyze social patterns, this approach helps us decode the complexities of human interactions. With our widespread use of online apps and social media platforms, researchers and analysts now have access to vast amounts of data that can be used to answer important social science questions.
Let’s return to our example. A computational social scientist can estimate and predict epidemics using Google searches. Google is often the first place we turn when we have symptoms, long before we take a test to confirm. Computational social science would map Google trends with officially documented outbreaks to predict cases that have not yet been confirmed and to anticipate where an outbreak may occur. By comparing the Google searches in areas with confirmed cases to similar Google searches happening in other regions, this method can help accurately predict disease spread.1
In this example, computational social science filled the gap of confirmed outbreaks through other available data, as not everyone will get a test when they feel sick. This approach enables us to make links between data and human behavior to understand complex social phenomena. As technology continues to evolve through advancements in machine learning and AI, computational social science can also help us understand how such algorithms are shaping our behavior and society.2
“Given the complexities of modern society, both domestically and internationally, the development of sophisticated tools in the computational social sciences will assume increasing importance.”
Kim L. Boyer, Interim Dean of the University at Albany’s College of Engineering and Applied Sciences3
About the Author
Emilie Rose Jones
Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible.
