UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Social welfare in the light of topic modelling

Baranowski, Mariusz; Cichocki, Piotr; McKinley, Jim; (2023) Social welfare in the light of topic modelling. Sociology Compass , Article e13086. 10.1111/soc4.13086. (In press). Green open access

[thumbnail of Social welfare in the light of topic modelling.pdf]
Preview
Text
Social welfare in the light of topic modelling.pdf - Published Version

Download (2MB) | Preview

Abstract

With an increased focus on social well-being in response to a burgeoning global economy exposing the weaknesses of social welfare policies, research output in the field has grown exponentially. Keeping track of the evolving research themes proves difficult due to the steady rise in the number of studies published in the interdisciplinary field of social welfare. Therefore, researchers need a comprehensive overview to confirm the current shape of the field based on the published research. Using a latent Dirichlet allocation algorithm as a topic modelling technique, this study identified 12 prominent themes from more than 10,000 research outputs on social welfare published from 2000 to 2020 in Scopus-indexed journals. Such an exploratory text-mining approach to literature review provides broad insights into the diversity of research and may serve as a foundation for further in-depth studies. Identifying these 12 thematic areas and their sub-themes allows us to articulate the complexity and diversity of social welfare issues, which go far beyond the field of well-established welfare economics or social work. The study shows that the topic of ‘social welfare’ has not only evolved over time but has significantly broadened its meaning. It can no longer be solely synonymous with institutional social security. We contend that research in this area needs to take into account a broader and more systematic range of determinants constituting the dynamic character of social welfare.

Type: Article
Title: Social welfare in the light of topic modelling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/soc4.13086
Publisher version: https://doi.org/10.1111/soc4.13086
Language: English
Additional information: © 2023 The Authors. Sociology Compass published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords: Social Sciences, Sociology, automatic literature review, latent Dirichlet allocation (LDA), machine learning, natural language processing, social welfare, WELL, SOCIOLOGY, SCIENCE, STATE
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/10168394
Downloads since deposit
54Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item