Find the cause of periodic behaviour present in Google-search "war" If you use Google Trends (a tool to illustrate the frequency of search terms) and search for the word "war" with USA as geographic region, you will be presented with the graph below showing monthly data from 2004 - 2019 (I have added the dates for emphasis);

The trend appears to be very periodic, on the same scale as "job," which economists will often say is closely related to the annual economic cycle of employment. As can be seen in the graph below;

However, if you search for the word "peace," there is no such periodicity;

If you change the region to another English speaking country such as the United Kingdom, the periodicity of "war" is still somewhat present but less pronounced, appears to be decaying and is instead shifted to peak around September - November.
What would be an efficient way to determine the cause of the periodicity of "war" (search region USA)?
Since correlation does not imply causation, I don't know how to effectively tackle this problem mathematically. Any ideas/approaches would be much appreciated!

EDIT: As has been suggested in the answer/comments below there may be a simple connection with US holidays. For example, searching for "war,memorial day" yields the following graph;


 A: I don't think this question has a definitive answer, but it sure looks like a school-term pattern to me.
x <- read.csv("multiTimeline.csv",stringsAsFactors=FALSE,skip=1)
x0 <- as.numeric(x[,2])    ## extract counts
xx <- ts(x0,frequency=12)  ## convert to time series
ss <- stl(xx, s.window="periodic")  ## seasonal decomposition
seas <- ss$time.series[,1]          ## extract seasonal component
monthplot(seas, labels=month.abb)


The pattern is lowest June-August (summer vacation); rises almost steadily from September to May, with a dip in December (winter vacation) ...
You could try to dig deeper: Google trends allows you to disaggregate by state, so you could try to see whether state-to-state variations are associated with differences in school calendars (could be tricky since school calendars probably vary as much within states as between ...)
You could try to come up with a "causal identification strategy" by finding times/locations where the school calendar shifted abruptly and see whether the Google Trends data changes in the expected way ...
