Towards an Author-Topic-Term-Model Visualization of 100 Years of German Sociological Society Proceedings

TL;DR

This paper explores the use of Bayesian analysis of term frequencies in scientific texts to improve science mapping, specifically applied to 100 years of German Sociological Society proceedings.

Contribution

It introduces a novel Bayesian model that incorporates text features for scientometric analysis, extending traditional co-citation studies with text-based data.

Findings

Bayesian analysis of term frequencies provides meaningful insights into scientific communities.

The new model enhances science mapping by integrating textual information.

Initial results demonstrate the potential of text-based scientometric methods.

Abstract

Author co-citation studies employ factor analysis to reduce high-dimensional co-citation matrices to low-dimensional and possibly interpretable factors, but these studies do not use any information from the text bodies of publications. We hypothesise that term frequencies may yield useful information for scientometric analysis. In our work we ask if word features in combination with Bayesian analysis allow well-founded science mapping studies. This work goes back to the roots of Mosteller and Wallace's (1964) statistical text analysis using word frequency features and a Bayesian inference approach, tough with different goals. To answer our research question we (i) introduce a new data set on which the experiments are carried out, (ii) describe the Bayesian model employed for inference and (iii) present first results of the analysis.