Paul W. Thurner

Daniel Racek, Brittany I. Davidson, Paul W. Thurner et al.

The use of language is innately political and often a vehicle of cultural identity as well as the basis for nation building. Here, we examine language choice and tweeting activity of Ukrainian citizens based on more than 4 million geo-tagged tweets from over 62,000 users before and during the Russian-Ukrainian War, from January 2020 to October 2022. Using statistical models, we disentangle sample effects, arising from the in- and outflux of users on Twitter, from behavioural effects, arising from behavioural changes of the users. We observe a steady shift from the Russian language towards the Ukrainian language already before the war, which drastically speeds up with its outbreak. We attribute these shifts in large part to users' behavioural changes. Notably, we find that more than half of the Russian-tweeting users shift towards Ukrainian as a result of the war.

P. Schulze, S. Wiegrebe, P. W. Thurner et al.

The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically relevant metadata. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself in an unsupervised fashion, usually by common topic models. A frequently used procedure to achieve this is the method of composition, a Monte Carlo sampling technique performing multiple repeated linear regressions of sampled topic proportions on metadata covariates. In this paper, we propose two modifications of this approach: First, we substantially refine the existing implementation of the method of composition from the R package stm by replacing linear regression with the more appropriate Beta regression. Second, we provide a fundamental enhancement of the entire estimation framework by substituting the current blending of frequentist and Bayesian methods with a fully Bayesian approach. This allows for a more appropriate quantification of uncertainty. We illustrate our improved methodology by investigating relationships between Twitter posts by German parliamentarians and different metadata covariates related to their electoral districts, using the Structural Topic Model to estimate topic proportions.