Big Data and Social Sciences: Epistemological and Methodological Challenges

In these last years, big data capture, processing and analysis have increasingly been experimented in various social science disciplines. Typical examples of big data that are relevant for social sciences are: the user-generated content created on blogs, social network sites and other online services; the tracks (digital footprints) that some human behaviors leave on the web or on other electronic archives. These new kinds of data sources are using to study traditional issues in social sciences: values, attitudes, opinion, decision makings, etc. The emergence of big data in social sciences seems to update some classical methodological debates. a. Unobtrusive vs. obtrusive data collection strategies. Big data sources exist independently of the research process; survey data are produced during the research process by interview, that – from the point of view of the interviewee – can be consider a quite artificial interaction process. According to several researchers, this marks a point ahead of big data on survey in terms of veracity. b. The role of theory in social research. Big data research, some have argued, implicitly assumes that the Web provide so much data that theoretical thinking become irrelevant. Everything, from this point of view, becomes understandable without theories and hypotheses, since the data speak for themselves. Although this idea is not entirely uncontested, it opens the way to new form of inductivism. c. Representativeness of data and generalization of findings. Some researchers argue that big data are, in principle, exhaustive in scope and, therefore, they allow the scientists to overcome all the vulnerabilities of sampled data. On the other hand, big data for social sciences are typically Web-based, so they bring about the problem of the relation between on-line and off-line activities. What is the impact of these and similar methodological issues on the way in which big data social researches are concretely carried out? I will try to answer to this question by surveying a sample of big data researches drawn from the areas of social sciences where they are more widespread