What is computational social science?

Researchers gather at the University of Sydney to develop state-of-the-art projects at Australia’s first Summer Institute in Computational Science.

What is Computational Social Science?

Online apps and social media platforms now track an unprecedented amount of data. As a result, data on online social behaviour is now almost infinitely available to researchers and analysts. In the discipline of computational social science, experts use this data to respond to crucial social science queries. They need to develop their programming and data analysis skills in order to do this well.

Why does it matter?

The motivations guiding those posts can be analysed and computationally processed to provide estimations relating to human social behaviour when people leave a digital footprint that is captured in the form of text, photographs, interaction metrics, and, of course, metadata. To better comprehend human culture in the digital age, researchers combine already-existing massive data sets from sources like Twitter, comments on blog posts, or key words in the media with small-scale randomised survey results. These technologies then assist researchers in providing answers to issues that are crucial to businesses, NGOs, government agencies, and society at large.

Poverty

Computational social science has a wide range of uses and can shed light on universal problems like poverty. According to the UN, seven percent is anticipated to be the global poverty rate in 2030. Global poverty reduction is seriously threatened by the ongoing COVID-19 crisis, violent conflict, and climate change. Lack of reliable data is another major obstacle in the fight against poverty. National statistics systems that reveal where and how severely individuals are suffering from poverty are lacking in many developing nations.

To fill this gap, researchers working in computational social science have found ways to provide estimates of levels of wealth tied to residential locations through the analysis of mobile phone metadata. Drawing upon existing metadata from the call logs of 1.5million mobile phone users in Rwanda, researchers were able to analyse the locations of incoming and outgoing calls in connection to a randomised survey of 1000 mobile phone users to build a model that can estimate levels of poverty in residential locations.

The estimates developed by computational social science researchers can be used by policy makers and NGOs to direct aid and development strategies towards targeted areas showing the highest levels of poverty. By combining questions related to social justice with big data, this research is scalable and can be applied to a range of different contexts and social settings.

Changing weather

Australia faced months of devastating bushfires in the summers of 2019 and 2020. The Australian bushfires caused almost 24 million hectares to burn, 33 human deaths, nearly 3 billion killed or displaced animals, and major public discussion on social media. Many scientists, politicians, and climate activists believed that the extent of the destruction would spur action on climate change during the fires and immediately afterward.

Researchers in computational social science aimed to better understand the connection between the bushfires and beliefs about climate change in reaction to this disaster. Researchers were able to identify and track key terms and hashtags to evaluate views of blame, causality, levels of emergency, and prevention strategies connected to the bushfire catastrophe using a dataset of 9000 tweets. By doing this, they were able to use science to analyse a contentious and emotive issue.

Despite the propagation of some false information, they discovered that Twitter activity related to the Australian bushfires strengthened support for taking action to combat climate change. Analysis revealed that people participating in bushfire-related Twitter arguments tended to discredit those who were thought to be to blame for the fires, such as politicians who deny climate change. In addition to being able to see an increase in conversations linking the fires to climate change, the nature of these conversations was also used to attract attention to the problem at hand.