Twitter Facial Analysis Reveals Demographics of Presidential Campaign Followers
If you follow Hillary Clinton or Donald Trump on Twitter, your face has probably been analyzed by a machine to determine your age, ethnicity, and social influence.
When Barack Obama won the 2008 presidential election, a crucial part of his campaign turned on his radical use of Facebook and Twitter to secure a substantial following. Today, social media is a powerful weapon in the armory of all candidates on the 2016 election trail.
And that raises an interesting question—how do the social media statistics of the leading candidates stack up against each other. Today we get an answer of sorts thanks to the work of Yu Wang and pals at the University of Rochester, New York, who have analyzed the Twitter follower demographics of Donald Trump and Hillary Clinton.
At the time of writing, Donald Trump had seven million followers and Hillary Clinton 5.7 million. What kind of information can be garnered from Twitter about these people?
The analysis by Wu and co is straightforward. They study both candidates’ followers, their user names, geographical information and each follower’s number of followers to determine their influence. In particular, they look at each user’s picture and use a machine-learning program to determine whether it shows a male or female and to identify that person’s ethnicity.
The results are simple to state. An interesting angle is the gender balance of Clinton’s followers. While Clinton enjoys substantial female support among politicians, Wu and co say there is good evidence that her support among average Democratic women has fallen sharply.
However, this does not seem to have influenced the gender balance among her supporters on Twitter—women make up 45 percent of her followers.
Trump, however, has an almost identical level of support at 45 percent. “Apparently Trump’s feud with Megyn Kelly has not alienated female voters,” say Wu and co.
The racial diversity among the followers is a different story. Wu and co say that Clinton’s supporters are more likely to be African American or Hispanic than Trump’s, who are more likely to be white. “This pattern in Twitter sphere is consistent with historical voting patterns,” they say.
The analysis of followers’ ages is put in perspective by the stereotypical idea that Republican Party followers tend to be old white people. And indeed, Wu and co say Trump’s followers contain more old people than Clinton’s. However, Trump also has more very young people, although many of them do not appear to be old enough to vote. Clinton has a stronger presence among the 18 to 40 age group.
The analysis of the social status of each candidate’s followers is curious. Wu and co do this simply by counting their number of followers, using the assumption that people with higher social status have more followers. “We find that individuals with only a few followers and individuals with hundreds of followers make up a larger share in the Trump camp than in the Clinton camp, while by contrast individuals with a few dozen to 200 followers have a larger presence among the Clintonists,” say Wu and co.
That’s an interesting approach that has plenty of potential. Facial analysis can clearly provide significantly more detail than is available from the other information available on Twitter. There is clearly more gold in them thar hills.
In particular, it would be interesting to see how the follower profiles for each candidate vary throughout the campaign and, in particular, in response to events in real time.
Wu and co offer no sign that they plan future analyses like this. But the candidates themselves must have their own social media experts—perhaps on the strength of this, they’ll want to include facial analysis in their armories.
Ref: arxiv.org/abs/1603.03097: Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
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