Scraping The Social?

This paper investigates the device of scraping, a technique for the automated capture of online data, and its application in social research. We ask how this 'medium-specific' technique for data collection may be rendered analytically productive for social research. We argue that, as a technique that is currently being imported into social research, scraping has the capacity to re-structure research in at least two ways. Firstly, as a technique that is not native to social research, scraping risks introducing 'alien' analytic assumptions such as a pre-occupation with freshness. Secondly, to scrape is to risk importing into our inquiry categories that are prevalent in the social practices and devices enabled by online media: scraping makes available already formatted data for social research. Scraped data, and online social data more generally, tend to come with analytics already built in. The pre-ordered nature of captured online data is often approached as a 'problem', but we propose it may be turned into a virtue, insofar as data formats that have currency in the practices under scrutiny may serve as a source of social data themselves. Scraping, we propose, makes it possible to render traffic between the object and process of social research analytically productive. It enables a form of 'live' social research, in which the formats and life cycles of online data may lend structure to the analytic objects and findings of social research. We demonstrate this point in an exercise of online issue profiling, and more particularly, by relying on Twitter and Google to track the issues of 'austerity' and 'crisis' over time. Here we distinguish between two forms of real-time research, those dedicated to monitoring live content (which terms are current?) and those concerned with analysing the liveliness of issues (which topics are happening?).