Datafication Research (1994-2023): Three Decades of Evolving Methodology in Data Science

This study maps the evolution of research themes on datafication, analyzing trends, key authors, interdisciplinary collaborations, and emerging topics from 1994 to 2023. The analysis reveals a notable increase in publication volume, particularly from 2014 onwards, reflecting advancements in digital technologies and heightened interest in data-driven research. A significant surge occurred during the COVID-19 pandemic, with 26.10% of total publications in 2022 and 30.52% in 2023 alone. Thematic clusters identified through keyword mapping include Social Media and Privacy, Artificial Intelligence and Machine Learning, Human Dimensions, and Infrastructure and Trust, highlighting diverse research foci. Emerging discussions on data justice and inequality reflect growing attention to the ethical and socio-political implications of datafication. The study also examines the types of documents and subject areas, revealing the dominance of peer-reviewed journal articles (71.41%) and a strong representation of social sciences (46.93%), computer science (14.75%), and arts and humanities (11.57%). Interdisciplinary connections underscore the broad impact of datafication across technology, healthcare, education, and media studies. This research offers insights into the dynamic nature of datafication, pointing to the need for further interdisciplinary collaboration, especially in addressing societal and ethical concerns such as data governance and digital inequality. Future research directions should focus on the human dimensions of datafication, data literacy, and the development of robust data governance frameworks to mitigate potential inequalities and power imbalances in a rapidly data-driven world.