Dataset Information

The research topic landscape in the literature of social class and inequality.

ABSTRACT: The literature of social class and inequality is not only diverse and rich in sight, but also complex and fragmented in structure. This article seeks to map the topic landscape of the field and identify salient development trajectories over time. We apply the Latent Dirichlet Allocation topic modeling technique to extract 25 distinct topics from 14,038 SSCI articles published between 1956 to 2017. We classified three topics as "hot", eight as "stable" and 14 as "cold", based on each topic's idiosyncratic temporal trajectory. We also listed the three most cited references and the three most popular journal outlets per topic. Our research suggests that future effort may be devoted to Topics "urban inequalities, corporate social responsibility and public policy in connected capitalism", "education and social inequality", "community health intervention and social inequality in multicultural contexts" and "income inequality, labor market reform and industrial relations".

SUBMITTER: Guo L

PROVIDER: S-EPMC6028105 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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The research topic landscape in the literature of social class and inequality.

Guo Liang L Li Shikun S Lu Ruodan R Yin Lei L Gorson-Deruel Ariane A King Lawrence L

PloS one 20180702 7

The literature of social class and inequality is not only diverse and rich in sight, but also complex and fragmented in structure. This article seeks to map the topic landscape of the field and identify salient development trajectories over time. We apply the Latent Dirichlet Allocation topic modeling technique to extract 25 distinct topics from 14,038 SSCI articles published between 1956 to 2017. We classified three topics as "hot", eight as "stable" and 14 as "cold", based on each topic's idio ...[more]

PMID: 29965983

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Project description:BackgroundAnnotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.MethodsOur proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research.ResultsWe validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner.ConclusionsOur approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets.

Dataset Information

The research topic landscape in the literature of social class and inequality.

Publications

The research topic landscape in the literature of social class and inequality.

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