Social big data and computational sociology and social sciences

Abstract

The research on what happens on the Internet often brings us closer to studies at the frontier of knowledge. This article is part of the transdisciplinary space of Computational Sociology and Social Sciences. It pretends to present the current development of research referred to as Social Big Data. It describes methodological processes typical in this field where social media on the Internet are the primary data source. Along with this description, it highlights some of the advantages, limitations, and challenges for research in this field, closely linked to methodological and technical advances made in other sciences. The text introduces Social Big Data's conceptual specificity as a confluence of social media, data analysis, and massive data. Then, we explore essential changes in the research process in this field and advances in working with social big data in areas of artificial intelligence such as machine learning, artificial neural networks, and deep learning, which are aligned with social sciences that tend to predictions. We then argue about the relevance for Sociology and other sciences to advance in an approach based on mixed methods in the Social Big Data area, rethinking the micro-macro link in this field of study. Through a case study [on the negationist social movement in Twitter in Spain during the COVID-19 pandemics], we also illustrate some potentials and limitations of this type of research, which will allow us to outline some of the methodological challenges that experts could incorporate into a research agenda in this area. Social Big Data and Computational Sociology and Social Sciences research offer directions of great interest to the Sociology of the coming years in which exceptional progress can be made in the development of transdisciplinarity and hybridization in science, enriching them.