SAGE Ocean Recommended Research


Welcome to the first edition of the SAGE Ocean Recommended Research Blog. Each month we’ll be sharing a cross-discipline round-up of big data research from SAGE Journals. We aim to make it easier for those interested in computational social science and big data analysis to read contemporary research, sparking discourse, and highlighting the sheer breadth of computational analysis being published across a variety of academic disciplines by SAGE. An overview of some of the papers can be found below. Head over to the Recommended Research page to see the full collection.

The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics.
Big Data & Society.

With cross-discipline big data analysis in mind, where better to kick things off than with a recent article published in Big Data & Society by Andrew Bartlett et al. The paper asks ‘who is currently poised to be the legitimate interpreter of Big Data social science’ as disciplines engage computational practices, data analysis and data interpretation in different ways. The authors go on to critique the distinction between crafted and found data across fields and how this leads to different enactments of big data. 

Fields such as physics have a long history in computational mathematical methodologies and unlike many traditional social science subjects. Bartlett et al make an important point that large data driven research should not be seen as a replacement of current social science practices, but sit alongside traditional forms.

Developments in computation and access to large data sets (as well as pre-existing hierarchies) have meant that sociologists and other social scientists face challenges to be the legitimate interpreters of social data in ways that biologists and physicists do not.

However, it is clear that the advent of data-intensive societies opens up new challenges and possibilities for social scientists and SAGE Ocean strives to support social scientists with the skills, tools and resources they need to work with big data and new technology.

Evaluating the services and facilities of European cities using crowdsourced place data.
Environment and Planning B: Urban Analytics and City Science.

Spyratos and Stathakis have analysed the services and facilities of European cities using crowdsourced data with a view to providing urban planners with the data they need to tackle environmental and socio-economic problems prevalent in urban areas across the globe. Advantages of crowdsourced data include timeliness, global coverage, low cost, and collecting data types that cannot be collected using traditional mapping techniques.

Prediction, pre-emption and limits to dissent: Social media and big data uses for policing protests in the United Kingdom.
New Media & Society.

The collection and analysis of social media data for the purposes of policing forms part of a broader shift from ‘reactive’ to ‘proactive’ forms of governance in which state bodies engage in big data analysis to predict, pre-empt and respond in real time to a range of social problems. The premise is that through the massive collection of data that contemporary technologies allow for, it is possible to identify patterns, outline possible behavior and predict risk.

We've also added a couple of special issues to peruse, including an editorial in Journalism & Mass Communication Quarterly, whereby the comparison is made between Jorge Luis Borges' 'The Library of Babel' (an infinite Library whose books contained all knowledge in the Universe) and the Big Data Library of today. An exciting if slightly quixotic lens into big data's future but a wonderful notion nonetheless.