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How to Leverage Social Data to Predict the Election

How to Leverage Social Data to Predict the Election Jon Elvekrog

With social network membership swelling, analysts are becoming ever more aware that social media can predict certain cultural events and decisions. This is readily apparent in the film industry, where social chatter, trending hash tags, and early viewer sentiment are easy barometers for determining whether a movie will be a blockbuster or a bomb.

Identifying box office returns is just the tip of the iceberg of what’s capable with social data. Analysis of social engagement data on a much deeper level can uncover unstated preferences and help match ad messages to consumers. It can even help identify which presidential candidate a user is likely to vote for.

With the presidential election ever closer, 140 Proof wanted to test the hypothesis that users identified as either liberal or conservative would be interact more readily with content that matched their political preferences. The study used historical data on social media engagement and current network associations to segment users into potential political parties and then record their engagement rates with political content.

Segmentation relies on publicly available social data, including user connections like friends and other accounts they follow. Fore example, you can safely place current @BarackObama followers in the liberal bucket. Shared content is crucial here too -- if someone responds negatively to a televised Obama speech or consistently shares content from conservative publications, slot them with the GOP.

For the purpose of this experiment, the Twitter users in the liberal, conservative and control groups were shown tweets (in the form of ad units) from both liberal and conservative news sources. To easily identify whether content skewed liberal or conservative, the messages were confined to the Presidential election. The content was culled from The Washington Post (liberal) and the Washington Times (conservative), and the Twitter accounts @TheDemocrats and @GOP (self explanatory). 

Anything critical of President Obama (Tweets that read “Infographic: Top 10 Obama Failed Promises”) was classified conservative. Salacious attacks on Romney (“Can Mitt Romney be likeable? Does he need to be?”) fell into the liberal bucket. Did we mention that we wanted to make these as clearly identifiable as possible?

The results showed that users are far more likely to engage with content hewing closely to their political beliefs than they are to unmatched political ads. Democrats engaged with liberal ads 6.35 percent of the time, compared to 3.97 percent for conservative ads. GOP supporters exhibited even higher engagement rate for their preferred content, with a 7.61 percent engagement rate, compared to 5.08 percent for liberal ads.

This shows that social media is a great way to politicians to galvanize users that match their political views, but it’s also vital information for brands and other social advertisers. Because social media users are far more likely to engage with relevant messaging, it’s fairly easy to move this study model from the political arena to the soda aisle to look at how hypothetical Coke and Pepsi fans interact with soda ad messages.

That’s not even the most interesting part. A full scientific study needs a control group, and this study compared the liberal and conservative engagement rates to those of apolitical Twitter users. The results were even more pronounced, with politically active users engaging with the ad content at nearly twice the rate as the apolitical users.

Again, brands can adopt this to their social strategies. It would suggest that soda drinkers are far more likely to engage in soft drink content than those who don’t drink soda, regardless of their brand preference. The same goes for athletic brands, where those who either bike or run will likely engage at higher rates than consumers who don’t exercise.

There is a wealth of untapped data readily available from social media channels, if you take the time to understand it. Predicting box office success on trending hashtags and buzz metrics is the low-hanging fruit of social. Segmenting the audience to understand how they’ll identify with messages can predict sales, and it may even predict a presidential election.

Jon is the CEO and co-founder of 140 Proof, the leading social advertising company. Along with his co-founder, Jon developed the patented Proof Interest Graph content matching technology to harness the data of the social stream to more accurately...

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