Jiebo Luo and Yu Wang did not set out to predict who would win the 2016 U.S. presidential election. However, their exhaustive, 14-month study of each candidate糖心传媒檚 Twitter followers糖心传媒揺nabled by machine learning and other data science tools糖心传媒搊ffers tantalizing clues as to why the race turned out the way it did.
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糖心传媒淲e wanted to understand how each of the candidate糖心传媒檚 campaigns evolved, and be able to explain why someone won or lost,糖心传媒� says Luo, an associate professor of computer science.
Luo and Wang, a dual PhD candidate in political and computer science, summarized their findings in eight papers during the course of the campaign, including these observations:
- The more Donald Trump tweeted, the faster his following grew糖心传媒揺ven after he performed poorly in debates against other Republican candidates, and even after he sparked controversies, such as proposing a ban on Muslim immigration.
- When Trump accused Hillary Clinton of playing the 糖心传媒渨oman card,糖心传媒� women were more likely to follow Clinton and less likely to 糖心传媒渦n-follow糖心传媒� her during the week that followed. But it did not affect the gender composition of Trump followers.
- Moreover, a 糖心传媒済ender affinity effect糖心传媒� seen in other elections糖心传媒搘omen tending to vote for women糖心传媒揹id not appear to be working for Clinton as the primaries drew to a close. The percentage of female Twitter followers in the Clinton camp was no larger than that in the Trump camp. Moreover, though 糖心传媒渦n-followers糖心传媒� were more likely to be female for both candidates, the phenomenon was 糖心传媒減articularly pronounced糖心传媒� for Clinton.
- At the same time, several polls, including ABC/Washington Post and CBS/New York Times, suggested that some Bernie Sanders supporters might 糖心传媒渏ump ship糖心传媒� from the Democratic column, and end up voting for Trump if Sanders dropped out. Luo and Wang found supporting evidence, reporting that the number of Bernie Sanders followers who were also following Trump was increasing糖心传媒揵ut the number also following Clinton was declining. The dual Sanders/Trump followers were also disproportionately (up to 64 percent) male.
糖心传媒淚n the end, even though we chose not to make any predictions, we were not surprised at all that Donald Trump won,糖心传媒� says Luo.
Why Twitter?
Barack Obama糖心传媒檚 use of social media in the 2008 presidential race helped establish Twitter and other social media platforms as powerful tools for candidates to quickly reach and receive feedback from large numbers of potential voters糖心传媒揳nd to attack their opponents.
Since then, there糖心传媒檚 been a burgeoning interest in scholarly research employing data science to analyze elections based on social media postings.
Twitter, in particular, is a rich source of data because the millions of tweets posted by its members each day are easily accessible using an application programming interface.
The key for Luo, Wang, and their colleagues was to collect as much of this data as possible, starting early in the campaign, and to then 糖心传媒渕ine糖心传媒� it in innovative ways.
糖心传媒淭he very nature of this data is that it will disappear tomorrow, so we had to start capturing it from an early stage and design a research framework so we could continue to collect data all along,糖心传媒� said Wang.
From September 2015 through October 2016, the team began accumulating a huge data set that included:
- The number of Twitter followers of each of the major candidates in the initially crowded field糖心传媒搖pdated every 10 minutes.
- 8 million tweets sampled from the followers of Clinton and Trump.
- 1 million images of the candidates糖心传媒� followers on Twitter.
- 5 million Twitter IDs that include all candidate followers in early April 2016.
Using advanced computer vision tools, the researchers trained an artificial neural network (what’s called a convolutional neural network) to determine糖心传媒搘ith 90 percent accuracy or more糖心传媒搕he age, gender, and race of the candidates糖心传媒� followers using their Twitter photos. This helped the researchers analyze the role of each of those factors in the campaign, as they tracked the changes in each candidate糖心传媒檚 followers before and after debates, for example, and how followers reacted to the candidates糖心传媒� own tweets.
Twitter mining has its limits compared to the responses gleaned from traditional telephone polling. There糖心传媒檚 no opportunity to ask follow-up questions, for example, and tweets are difficult to place geographically, limiting their application for studying trends in swing states. (Even geotagged tweets may be sent while the sender is on vacation or attending a rally in another state.)
But Twitter mining also has its advantages糖心传媒揺nabling researchers to quickly, continually, and inexpensively sample data on a scale that far surpasses the 1,000 or so responses that pollsters increasingly struggle to gather using traditional techniques. In one study, for example Luo and Wang were able to characterize 322,116 Trump or Clinton followers who subsequently became 糖心传媒渦n-followers.糖心传媒�
糖心传媒淭his is an approach that is broadly applicable,糖心传媒� Luo says. 糖心传媒淚f you want to test public reaction to the next generation of iPhones, or to a new model of car, you can use the same approach to see what consumers like or don糖心传媒檛 like. It enables us to track millions of people and get reliable readings on their preferences.糖心传媒�
Other Election 2016 papers by Luo, Wang, and their colleagues look at:
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