Putting Data Science In The Service Of Social Science

By Carl Miller (@carljackmiller), Centre for the Analysis of Social Media, Demos 

The rise of social media has been important; that is no great revelation. It has wrought profound social change, buffeted our institutions and altered, for many of us, our way of life. New identities, dialects, cultures, affiliations and movements have all bloomed and spread across the digital world, and spilled out of it into mainstream public life.

Back in 2012, we at Demos could see that social media was changing research too. The transfer of social activity onto digital spaces was ‘datafying’ social life. Huge new datasets were being routinely created that we saw as treasure troves of behavioural evidence: often very large, in real-time, rich, linked and unmediated. It was a massive new opportunity to learn about how people and society worked.

Unlocking these datasets presented an enormous challenge. The sheer scale of social media data also meant that conventional social research methods couldn’t cope. Powerful new analytical techniques - modelling, entity extraction, machine learning, algorithmic clustering - were needed to make sense of what was happening. However, the true challenge wasn’t a technological one alone. It was how to deploy the new tools of data science in the service of social science. Getting better at counting people is not the same as getting better at understanding them. 

We established the Centre for the Analysis of Social Media that brought together social and policy researchers at Demos, and technologists from the University of Sussex with the explicit aim of confronting this challenge. The first layer of the challenge has been the technology itself. The tools of big data analysis needed to be put into the hands of non-technical researchers: the subject matter experts who have long understood social science, and now needed to be able to do it in a new way. We built a technology platform, Method52, which allowed non-technical users to use a graphical user interface, and drag-and-drop components to flexibly conduct big data analytics, rather than be faced with a screen full of code. Especially important was to make accessible a vitally important technique called natural language processing. Coupled with machine learning, it is one of the crucial ways of understanding bodies of primarily text-based data (like Tweets or Facebook posts) that are too large to manually read. 

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This article was originally published in the National Centre for Research Methods’ Newsletter 2016:2 - http://www.ncrm.ac.uk/news/methodsnews.php