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The Google Ngram Corpora seem to offer a unique opportunity to study linguistic and cultural change in quantitative terms. To avoid breaking any copyright laws, the data sets are not accompanied by any metadata regarding the texts the corpora consist of. Some of the consequences of this strategy are analyzed in this article. I chose the example of measuring censorship in Nazi Germany, which received widespread attention and was published in a paper that accompanied the release of the Google Ngram data (Michel et al. (2010): Quantitative analysis of culture using millions of digitized books. Science, 331(6014): 176–82). I show that without proper metadata, it is unclear whether the results actually reflect any kind of censorship at all. Collectively, the findings imply that observed changes in this period of time can only be linked directly to World War II to a certain extent. Therefore, instead of speaking about general linguistic or cultural change, it seems to be preferable to explicitly restrict the results to linguistic or cultural change ‘as it is represented in the Google Ngram data’. On a more general level, the analysis demonstrates the importance of metadata, the availability of which is not just a nice add-on, but a powerful source of information for the digital humanities.
Recently, a claim was made, on the basis of the German Google Books 1-gram corpus (Michel et al., Quantitative Analysis of Culture Using Millions of Digitized Books. Science 2010; 331: 176–82), that there was a linear relationship between six non-technical non-Nazi words and three ‘explicitly Nazi words’ in times of World War II (Caruana-Galizia. 2015. Politics and the German language: Testing Orwell’s hypothesis using the Google N-Gram corpus. Digital Scholarship in the Humanities [Online]. http://dsh.oxfordjournals.org/cgi/doi/10.1093/llc/fqv011 (accessed 15 April 2015)). Here, I try to show that apparent relationships like this are the result of misspecified models that do not take into account the temporal aspect of time-series data. The main point of this article is to demonstrate why such analyses run the risk of incorrect statistical inference, where potential effects are both meaningless and can potentially lead to wrong conclusions.
Corpus-assisted analyses of public discourse often focus on the level of the lexicon. This article argues in favour of corpus-assisted analyses of discourse, but also in favour of conceptualising salient lexical items in public discourse in a more determined way. It draws partly on non-Anglophone academic traditions in order to promote a conceptualisation of discourse keywords, thereby highlighting how their meaning is determined by their use in discourse contexts. It also argues in favour of emphasising the cognitive and epistemic dimensions of discourse-determined semantic structures. These points will be exemplified by means of a corpus-assisted, as well as a frame-based analysis of the discourse keyword financial crisis in British newspaper articles from 2009. Collocations of financial crisis are assigned to a generic matrix frame for ‘event’ which contains slots that specify possible statements about events. By looking at which slots are more, respectively less filled with collocates of financial crisis, we will trace semantic presence as well as absence, and thereby highlight the pragmatic dimensions of lexical semantics in public discourse. The article also advocates the suitability of discourse keyword analyses for systematic contrastive analyses of public/political discourse and for lexicographical projects that could serve to extend the insights drawn from corpus-guided approaches to discourse analysis.