Text Analysis Predicts Your Politics Without Asking
How What You Say Says Way More Than What You Said
Pretend for a moment that you had a pen pal overseas and they asked you to describe yourself. What would you tell them? What makes you “you”?
It turns out that which traits, characteristics and aspects of your identity you choose to focus on may say more than you realize.
For instance, they can be used to predict whether you are a Democrat or a Republican.
With the U.S. presidential race underway in earnest, I thought it would be interesting to explore what if any patterns in the way people describe themselves could be used to identify their political affiliation.
So we posed the question above verbatim to a nationally representative sample of just over n=1000 (sourced via CriticalMix) and ran the responses through OdinText.
Not surprisingly, responses to this open-ended question were as varied as the people who provided them, but OdinText was nevertheless able to identify several striking and statistically significant differences between the way Republicans and Democrats described themselves.
NOT About Demographics
Let me emphasize that this exercise had nothing to do with demographics. We’re all aware of the statistical demographic differences between Republicans and Democrats.
For our purposes, what if any specific demographic information people shared in describing themselves was only pertinent to the extent that it constituted a broader response pattern that could predict political affiliation.
For example, we found that Republicans were significantly more likely than Democrats to say they have blonde hair.
Of course, this does not necessarily mean that someone with blonde hair is significantly more likely to be a Republican; rather, it simply means that if you have blonde hair, you are significantly more likely to feel it noteworthy to mention it when describing yourself if you are a Republican than if you are a Democrat.
Self-Image: Significant Differences
OdinText’s analysis turned up several predictors predictors for party affiliation, here are 15 examples indexed below.
- Republicans were far more likely to include their marital status, religion, ethnicity and education level in describing themselves, and to mention that they are charitable/generous.
- Democrats, on the other hand, were significantly more likely to describe themselves in terms of friendships, work ethic and the quality of their smile.
Interestingly, we turned up quite a few more predictors for Republicans than Democrats, suggesting that the former may be more homogeneous in terms of which aspects of their identities matter most. This translates to a somewhat higher level of confidence in predicting affinity with the Republican Party.
As an example, if you describe yourself as both “Christian” and “married,” without knowing anything else about you I can assume with 90% accuracy that you vote Republican.
Again, this does not mean that Christians who are married are more than 90% likely to be Republicans, but it does mean that people who mention these two things when asked to tell a stranger about themselves are extremely likely to be Republicans.
While this exercise was exploratory and the results should not be taken as such, it demonstrates that text analytics make it entirely possible to read between the lines and determine far more about you than one would think possible.
Obviously, there is a simpler, more direct way to determine a respondent’s political affiliation: just ask them. We did. That’s how we were able to run this analysis. But it’s hardly the point.
The point is we don’t necessarily have to ask.
In fact, we’ve already built predictive models around social media profiles and Twitter feeds that eliminate the need to pose questions—demographic, or more importantly, psychographic.
Could a political campaign put this capability to work segmenting likely voters and targeting messages? Absolutely.
But the application obviously extends well beyond politics. With an exponentially-increasing flood of Customer Experience Feedback, CRM and consumer-generated text online, marketers could predicatively model all manner of behavior with important business implications.
One final thought relating to politics: What about Donald Trump, whose supporters it has been widely noted do not all fit neatly into the conventional Republican profile? It would be pretty easy to build a predictive model for them, too! And that could be useful given the widespread reports that a significant number of people who plan to vote for him are reluctant to say so.