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

Introduction Author co-citation studies (Zhao & Strotmann, 2008) employ factor analysis to reduce highdimensional 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 allows for 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 the 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.