Sociology between big data and research frontiers, a challenge for educational policies and skills

Abstract

The paper focuses on the challenges posed within sociology and social research by the transformations created by the “data society”. To this end, the paper outlines some of the most significant elements for new frontier research which sociology is forced to confront also in relation to the challenges for educational policies and skills. While leading literature decries the need to promote alphabetising data, otherwise defined as data literacy , the idea proposed here is that it is necessary to work towards understanding data . An understanding that highlights the role sociology has firmly set itself since its foundation, that of studying and explaining the complexity of the relationships that characterize social life in every context and period.

The role of open data in digital society: The analysis of scientific trending topics through a bibliometric approach

“…Hence, the results obtained here indicate how the pervasiveness of data requires a significant ability to know and interpret their language and thus of the complex set of theoretical, logical, and operational steps that link abstract concepts to their empirical correlates through a process of signification. In line with the findings of other studies, the aptitude to combine different languages and points of view seems to be necessary to understand the information capacity of this type of data, and consequently to be able to support political and governance processes and choices of collective interest at all levels (Capogna, 2022).…”

“…This is followed by publications on more specific topics related to methodological issues and procedures related to open data ( quality, data science , and replicability, reproducibility , and evaluation , as well as knowledge, science, big data, cities, impact , and challenges ), as well as those influencing new forms of knowledge. In particular, these studies dealt with how digital data, such as open data and big data, can redefine the key questions that drive scientific knowledge, exploring measurement conventions (Salais, 2016 ), the processes involved in building automated detection systems, and the issues of internal consistency of the theoretical-methodological framework (Capogna, 2022 ).…”

“…Lastly, the quadrant above the others identifies a group of keywords (marked in blue) related more closely to dealing with the basic prerequisites and standards that characterize the scientific method associated with the study and application of open data in terms of data quality, data science, replicability, reproducibility, and evaluation, etc. For example, the scientific debate in the field of social sciences is dealing with the question of data quality and the reproducibility of digital data-and also open data-produced in the digital platforms, understood as a social artifact with certain logics, aims and constraints, often not determined and not accessible by the researcher (Capogna, 2022). To better understand the conceptual structure underlying the study of open data over time and analyse the evolution of the topics in the 10-year period, a thematic plot was created (Figure 4).…”

Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

“…Zhang built an educational resource network, the platform based on attracting commercial resources, and built a bridge between teachers and resource service units to match supply and demand [2]. Capogna provided courses of all grades and disciplines in primary and secondary schools to primary and secondary school students in the form of online videos, which further enriched the supply of high-quality educational resources and made a new exploration and leap forward in improving the service mode of public education [3]. Cai et al think that with the advent of data-intensive era, data sharing becomes very important, which provides a theoretical basis for researchers to reanalyze, reduces their repetitive work, and thus accelerates scientific research innovation [4].…”

New horizons in the sociology of sport

“…To give one example (42): even if it is possible to collect billions of data about sentiments of football fans' tweets, the findings regarding collective emotionality in football still remain superficial if the tweets cannot be contextualized against the background of discursive strategies on Twitter, emotional contagion in larger groups, the typical "language" of fans in this sport (or in other words, theoretical sociological reflections on the dynamics of collective emotions in sports), as well as the large-scale, social structural processes (such as globalization, commodification, securitization, mediatization, and postmodernization) that have reshaped elite-level global football over the past few decades (43). On the other hand, to avoid an uncritical approach to the results of big data surveys, it is necessary to figure out "the sociotechnical processes involved along the "data building chain"" (44). Data does not just appear out of thin air.…”