Despite the challenges, there are many interesting examples where social research communities in the UK are engaging with big data.
The social research institute NatCen leads a network for people using or seeking to use social media in social science research. Researchers contribute to a blog about methods and practice, with some very practical insights. Such as this account of a discussion on big data in social science, covering ethics, skills, access to datasets and software and interdisciplinary working. A table of examples of how one researcher has used different digital and social media tools in her work. And ten things survey researchers should know about Twitter.
This podcast introduces the work of Demos Centre for Analysis of Social Media, where social and data scientists work together to explore the role of social media in social life, and has a good, brief overview of ethical and technical challenges. The Demos Centre has a work stream in progress for improving social media research and have produced some interesting content on grounded theory and big data, which I cover in more detail in a separate post.
The Data Science Lab at Warwick University draws on approaches from the social sciences, the natural sciences and engineering, for projects broadly concerned with the insights that big data offers into human behaviour and decision making. For example, quantifying the relationship between environmental aesthetics and human health, using a website to crowdsources ratings of “scenicness” for geotagged photographs across Great Britain, in combination with data on citizen-reported health from the Census for England and Wales.
The example I found most exciting is from the Social Data Science Lab team at Cardiff University (although with an MSc in Criminology from Cardiff I may be biased!). In this project project they combined exactly what is being called for elsewhere: a multi-disciplinary team, big data (Twitter), conventional data sources (a database of police-recorded crime provided by the Metropolitan Police Service & the UK Census 2011) and theory driven analytics, to test whether tweets containing mentions of the breakdown of the local social and environmental order (often referred to as ‘broken window’ theory) were more predictive of crime than conventional correlates such as unemployment and proportion of young people in an area. I would really recommend reading this article for more detail, it brings to life the ‘doing’ of big data in social sciences, the challenges and opportunities in these new sources of data, and early stage computational criminology methods.
All of these examples discussed here are evolving and resolving issues at a fast rate and need a watching brief. Looking for individuals to collaborate with who are part of a new tradition of including data science in their social research toolkit is also a next step. The final take-away being how big data sources on their own are limited; traditional sources of administrative data are still very relevant and I cover them in my next post.