100% of statisticians would say this is a terrible method for predicting elections. However, in the case of 2016’s presidential election, analyzing the geographic search volume of a few telling keywords “predicted” the outcome more accurately than Nate Silver himself.

The 2016 US Presidential Election was a nail-biter, and many of us followed along with the famed statistician’s predictions in real time on FiveThirtyEight.com. Silver’s predictions, though more accurate than many, were still disrupted by the election results.

In an effort to better understand our country (and current political chaos), I dove into keyword research state-by-state searching for insights. Keywords can be powerful indicators of intent, thought, and behavior. What keyword searches might indicate a personal political opinion? Might there be a common denominator search among people with the same political beliefs?

It’s generally agreed that Fox News leans to the right and CNN leans to the left. And if we’ve learned anything this past year, it’s that the news you consume can have a strong impact on what you believe, in addition to the confirmation bias already present in seeking out particular sources of information.

My crazy idea: What if Republican states showed more “fox news” searches than “cnn”? What if those searches revealed a bias and an intent that exit polling seemed to obscure?

The limitations to this research were pretty obvious. Watching Fox News or CNN doesn’t necessarily correlate with voter behavior, but could it be a better indicator than the polls? My research says yes. I researched other media outlets as well, but the top two ideologically opposed news sources — in any of the 50 states — were consistently Fox News and CNN.

Using Google Keyword Planner (connected to a high-paying Adwords account to view the most accurate/non-bucketed data), I evaluated each state's search volume for “fox news” and “cnn.”

Eight states showed the exact same search volumes for both. Excluding those from my initial test, my results accurately predicted 42/42 of the 2016 presidential state outcomes including North Carolina and Wisconsin (which Silver mis-predicted). Interestingly, "cnn" even mirrored Hillary Clinton, similarly winning the popular vote (25,633,333 vs. 23,675,000 average monthly search volume for the United States).

In contrast, Nate Silver accurately predicted 45/50 states using a statistical methodology based on polling results.

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This gets even more interesting:

The eight states showing the same average monthly search volume for both “cnn” and “fox news” are Arizona, Florida, Michigan, Nevada, New Mexico, Ohio, Pennsylvania, and Texas.

However, I was able to dive deeper via GrepWords API (a keyword research tool that actually powers Keyword Explorer's data), to discover that Arizona, Nevada, New Mexico, Pennsylvania, and Ohio each have slightly different “cnn” vs “fox news” search averages over the previous 12-month period. Those new search volume averages are:



“fox news” avg monthly search volume “cnn” avg monthly search volume KWR Prediction 2016 Vote Arizona 566333 518583 Trump Trump Nevada 213833 214583 Hillary Hillary New Mexico 138833 142916 Hillary Hillary Ohio 845833 781083 Trump Trump Pennsylvania 1030500 1063583 Hillary Trump

Four out of five isn’t bad! This brought my new prediction up to 46/47.

Silver and I each got Pennsylvania wrong. The GrepWords API shows the average monthly search volume for “cnn” was ~33,083 searches higher than “fox news” (to put that in perspective, that’s ~0.26% of the state’s population). This tight-knit keyword research theory is perfectly reflected in Trump’s 48.2% win against Clinton’s 47.5%.

Nate Silver and I have very different day jobs, and he wouldn’t make many of these hasty generalizations. Any prediction method can be right a couple times. However, it got me thinking about the power of keyword research: how it can reveal searcher intent, predict behavior, and sometimes even defy the logic of things like statistics.

It’s also easy to predict the past. What happens when we apply this model to today's Senate race?

Can we apply this theory to Alabama’s special election in the US Senate?

After completing the above research on a whim, I realized that we’re on the cusp of yet another hotly contested, extremely close election: the upcoming Alabama senate race, between controversy-laden Republican Roy Moore and Democratic challenger Doug Jones, fighting for a Senate seat that hasn’t been held by a Democrat since 1992.

I researched each Alabama county — 67 in total — for good measure. There are obviously a ton of variables at play. However, 52 out of the 67 counties (77.6%) 2016 presidential county votes are correctly “predicted” by my theory.

Even when giving the Democratic nominee more weight to the very low search volume counties (19 counties showed a search volume difference of less than 500), my numbers lean pretty far to the right (48/67 Republican counties):

It should be noted that my theory incorrectly guessed two of the five largest Alabama counties, Montgomery and Jefferson, which both voted Democrat in 2016.

Greene and Macon Counties should both vote Democrat; their very slight “cnn” over “fox news” search volume is confirmed by their previous presidential election results.

I realize state elections are not won by county, they’re won by popular vote, and the state of Alabama searches for “fox news” 204,000 more times a month than “cnn” (to put that in perspective, that’s around ~4.27% of Alabama’s population).

All things aside and regardless of outcome, this was an interesting exploration into how keyword research can offer us a glimpse into popular opinion, future behavior, and search intent. What do you think? Any other predictions we could make to test this theory? What other keywords or factors would you look at? Let us know in the comments.



Also, if you've enjoyed this post, check out Sam Wang's Google-Wide Association Studies! --Fascinating read.