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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.
We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use—both individually and collectively—over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
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.