The race for U.S. Republican presidential candidates is drawing record attention for soaring ratings and ad dollars. Whether it’s because the “entertainer” Donald Trump is a candidate or because it’s a more competitive race than ever before requiring two debates worth of candidates, it’s clear Americans are paying attention.
Social media data is a great indicator of the impact each candidate is making. It’s faster than polling numbers, captures the audience in real time, and boasts a much larger panel size than traditional methods. To analyze the results of Wednesday night’s debate on CNN, we turned to 4C’s Live Event and TV Analytics products.
Before the debate, Trump led in the polls and according to social data, he was also the frontrunner in terms of number of unique engagers. However, when comparing other metrics, Dr. Ben Carson is a formidable opponent. Dr. Carson bested Trump in social loyalty, the number of repeat engagers week-over-week and the number of engagements per unique engager.
Social media and TV data illuminate pivotal moments for each politician during the debate. Social engagement reveals the audience’s response to the candidates. Through 4C TV Analytics powered by Teletrax, we tracked the number of rebroadcasts for different segments of the debate on a second-by-second basis, showing which moments were most likely to influence voters’ perceptions of the political landscape.
Trump led in total speaking time and commanded the lion’s share of engagement for the night. Trump bore the brunt of most of the candidate’s offensives during the debate. Peak engagement for Trump was achieved early when Carly Fiorina called him an “entertainer” and called on voters’ “common sense” to decide whether Trump should have his finger on the nuclear codes.
Social engagement for Fiorina surged several times throughout the night, but she achieved the most engagement when she talked about defunding Planned Parenthood – a topic of particular interest. Those segments of the debate were rebroadcast more than 140,000 times shortly after the event.
Which candidates received the highest lift from their performance? Through 4C Affinities, the strength of the connection between each politician and the people who engaged with the event on social media can be measured.
Based on Penetration per Thousand (PPM), Chris Christie, Carly Fiorina, and Jeb Bush had the most notable lift. Governor Christie achieved 100 PPM during the debate, up 4x. Carly Fiorina surged to 156 PPM during the event, up 3.7x. Jeb Bush received 159 PPM, up 2.6x.
Donald Trump and Ben Carson had very small lifts, but both earned higher affinities during the event and had higher affinities leading into the event than the other candidates. Only Scott Walker’s affinity declined during the debate.
4C Affinities also show the politicians who have the strongest connection to one another in voters’ minds, thus highlighting where new votes can be sourced. Senator Rubio had better watch out. He’s a prime place to acquire some votes for top movers Chris Christie, Carly Fiorina, and Jeb Bush.
Social media and TV data provide unfiltered insights into how the presidential race is shaping up and the impact each of the candidates are making. 4C will continue to release new data as key milestones and events in the election cycle take place. The one thing we can confidently predict is that this will continue to be an entertaining process.
Glossary of Metrics
Social Engagement: A Mention, Tweet, Retweet, Post Like, Share, or Comment on Twitter and Facebook.
Unique Engagers: Number of individuals who had a social engagement with a politician.
Loyalty: Percentage of individuals who repeated engagements with a politician week-over-week.
Engagement Rate: Average number of social engagements per person.
Clip Rebroadcasts: Number of times a one second or longer clip was shown on another local or national TV network.
4C Affinities: Measure the strength of connection between brands, people, TV programs and events measured in Penetration Per Thousand (PPM).
Penetration Per Thousand (PPM): The number of shared unique engagers between two social entities, divided by the total number of unique engagers in the target. Ie: For every 1,000 people who engage with X, how many also engage with Y?