![]() ![]() Second, we show that the moral and non-moral language used by the various communities converged in interesting and informative ways. After the pandemic was officially declared, the interactions between these groups increased. Over the course of our study, both Republicans and Democrats entered the vaccine conversation in large numbers, forming coalitions with Antivaxxers and public health organizations, respectively. First, we find that authors cluster into several large, interpretable groups, and that the discourse was greatly affected by American partisan politics. We identify the actors, the language they use, how their language changed, and what can explain this change. This is an observational study of Twitter analyzing the impact that COVID-19 had on vaccine discourse. Trust in vaccination is eroding, and attitudes about vaccination have become more polarized. Extensive code is provided in Supplementary Materials. The method is illustrated by a case study that investigates philosophical content, broadly construed, in pre-1900 English-language New Zealand newspapers. The method is an ‘iterative bootstrapping’ approach in which candidate corpora are evaluated using text mining techniques, items are manually labelled, and Naïve Bayes text classifiers are trained and applied in order to produce new candidate corpora. This article presents such a method for historical newspaper archives digitized using the METS/ALTO XML standard (Veridian Software, n.d.). Methods for generating smaller specialized corpora from large archives are required to solve this problem. ![]() ![]() Even if it is computationally feasible to apply sophisticated language processing to an entire digital archive, if the material of interest is a small fraction of the archive, the results are unlikely to be useful. However, the scale of these archives can limit the direct application of advanced text processing methods. The availability of large digital archives of historical newspaper content has transformed the historical sciences. ![]()
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