This paper deals with the problem of deriving consistent time-series from newspaper contentbased topic models. In the first part, we recapitulate a few our own failed attempts, in the second one, we show some results using a twin strategy, that we call prototyping and seeding. Given the...

In this paper, we present a German-language Twitter dataset related to the Covid-19 pandemic. We show how the R (R Core Team 2020) package rtweet (Kearney 2019) and a combination of keywords can be used to create the dataset and provide a way to rehydrate most of the tweets. The dataset consists...

The current coronavirus pandemic is a recent example of how an unpredictable event can cause uncertainty and affect people, markets and economies worldwide. Economic indicators like the well-known and frequently cited Economic Policy Uncertainty Index, developed by the three economists Scott R....

In this paper, we present a new dynamic topic modeling method to build stable models and consistent time series. We call this new method RollingLDA. It has the potential to overcome several difficulties researchers, who use unsupervised probabilistic topic models, have grappled with: namely the...

In this paper we update our Uncertainty Perception Indicator (UPI) with data from the first quarter of 2021. UPI values have declined in recent quarters. At first glance this might come as a surprise, with the third Corona wave having less of an impact than the first one, even though the former...

Text mining is an active field of statistical research. In this paper we use two methods from text mining: the Poisson Reduced Rank Model (PRR, see Jentsch et al. 2020; Jentsch et al. 2021) and the Latent Dirichlet Allocation model (LDA, see Blei et al. 2003) for the statistical analysis of party...

In this paper we update our Uncertainty Perception Indicator (UPI) with data from the first quarter of 2021. UPI values have declined in recent quarters. At first glance this might come as a surprise, with the third Corona wave having less of an impact than the first one, even though the former...

This paper deals with the problem of deriving consistent time-series from newspaper contentbased topic models. In the first part, we recapitulate a few our own failed attempts, in the second one, we show some results using a twin strategy, that we call prototyping and seeding. Given the...