One of the challenges of applying data analytics in sociology is the institutionalization of data science outside of sociology, as the former expertise of sociology was based on its own method of research. Another challenge is epistemological in nature, relates to the noisiness and validity of digital data, and the question of explanation/causation, which is highly important for sociology. These challenges give the background of the tension between the Big Data based, social-related findings and the sociological skepticism questioning the potential of this knowledge-production. The challenges can be solved through the redefinition of the research methodological basis of sociology, by the organic incorporation of data science know-how to its own methods. The solution also needs the combined application of qualitative and quantitative analysis motives, and the use of knowledge-driven science instead of the data-driven approach.
Our research stream is motivated by a continuously growing social science interest in data science. As an example, see the case of automated text analytics: the following figure shows that the popularity of automated text analytics has been continuously growing in recent years in general and also in each discipline investigated (to access publication data we used Dimensions, https://dimensions.ai). Each trend line is growing persistently even after normalizing for the total number of publications in the discipline. The topic’s percentage portion in sociology increased faster than in sciences in general. In summary, automated text analytics is becoming an increasingly recognized approach in sociology.