I want to see if Wikipedia articles for topics featured in popular documentaries get a boost in page views on the day the documentary is aired. I then want to see if time on screen is a predictor of this boost.
I have to account for baseline popularity but I'm not sure how. For instance, the Wikipedia article for global warming will have a different average number of hits per day than the article for acid rain.
My working solution has been to use the yearly average Wikipedia hits for each article as a baseline. I will have data on the number of hits for each article from the day the documentary aired. So I can calculated a percentage change from baseline to airing date (
Article Wikipedia_baseline doc_hits %_change seconds_on_screen global_warming 300 450 50 60 acid_rain 100 260 30 160 plastics 250 600 75 140
But if I feed this into a GLM, I'm worried I'm being circular e.g.
glm(%_change ~ seconds_on_screen + Wikipedia_baseline)
%_change has been generated using the
Wikipedia_baseline but I obviously need to take the popularity of the articles into account.