Abstract

Big data is an emerging field of research for scholars in the social sciences and humanities, that opens great opportunities for capturing the dynamics of complex social phenomena, from protest movements, to emerging political parties, in profound ways. Yet, there are also serious risks involved in this development, as academics involved in the field, sometimes ignore the inherent bias of datasets, or do not dedicate sufficient attention to the social context of the data they analyse, and the cultural specificity of certain terms, hashtags, and contents conveyed via social media as Facebook and Twitter. This uncritical use of big data analysis has produced some serious scholarly blunders, at different levels, such as sampling (choosing the wrong hashtag!) and interpretation (filtering datasets in dubious ways). In this talk I will argue for the need to couple qualitative and quantitative methodologies in the study of social media and big data to avoid these mistakes being repeated in the future. Big data research calls for the formation of research teams with mixed expertise to couple the ability to crunch big data, with the capacity to take into account the “fine details” which are necessary to understand the semantic aspect of contemporary online communication.