COVID-17 explores how COVID-19 might have shifted Canadian perceptions of healthcare.

COVID-17 offers visitors the opportunity to compare news articles written during the first four months of the pandemic (January 21 to April 21) with GPT-2 generated articles. One can trace patterns over time by comparing the tonal (dis)similarities between each pair of articles. For example, by looking at how GPT-2 predicted the contents of an article with a headline discussing social distancing, one might note the AI is incapable of approaching the subject in a realistic manner – a consequence of the practice not being in the social consciousness of 2017.

The primary goal of COVID-17 is to assess the viability of using text analysis tools on artificially generated text. The output of models like GPT-2 is often overlooked. This is because the output is logically incoherent – without communicative intent. While the generated text may look coherent to a casual observer, closer inspection always reveals structural inconsistencies. However, this does not necessarily mean the output is without value. GPT-2 has reasons for choosing the words it does. Given enough examples, text analysis tools (which often ignore the structure) should be able to extract meaningful information. What sorts of information? Considering the static nature of models like GPT-2, one could conceivably treat them as time machines of public opinion. COVID-17 is an early exploration of this idea.