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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);

Trend of "war" in United States 2004 - 2019

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;

enter image description here

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

enter image description here

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;

enter image description here

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    $\begingroup$ People do not search for war during their vacations and/or when the weather is nice. Also, probably schools are closed. Be very careful about associating search patterns with the underlying activity of the metric. For example looking at when searches for different countries peaked, I was initially surprised to find out that searches for "Turkey" peaked in late October, early November. $\endgroup$ – usεr11852 says Reinstate Monic Mar 31 at 19:24
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    $\begingroup$ Memorial Day is in May. $\endgroup$ – Reinstate Monica Apr 1 at 3:43
  • $\begingroup$ Thank you for the insights, these are some interesting points. @Sycorax your idea has a good fit, even with just an eyeball inspection $\endgroup$ – litmus Apr 1 at 5:26
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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)

enter image description here

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 ...

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  • $\begingroup$ Thanks for the answer! It appears to be centered around Memorial day, which may be enhanced further by the school-term pattern as you suggested $\endgroup$ – litmus Apr 1 at 5:28
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    $\begingroup$ Maybe this could be tested by finding some instrumental variables---in this case searches not related to war but correlated with vacation? $\endgroup$ – kjetil b halvorsen Apr 1 at 17:17
  • $\begingroup$ it would also be interesting to see about decomposing the searches for "war": e.g. what are the seasonal patterns for "Revolutionary War", "Civil War", "World War (I | II)/(First | Second) World War", "Vietnam War", etc. ? Getting the data at a finer time resolution might help too. But causality will always be challenging. $\endgroup$ – Ben Bolker Apr 1 at 17:38

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