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Data Science Proves We Love to Hate-Watch TV

Study by startup Canvs finds 'hate' is the emotion most strongly associated with TV tune-in

One of the true pleasures of HBO’s “Game of Thrones” the last few seasons has been seething at the outrages committed by oleaginous psychopath Ramsay Bolton. And now, thanks to Big Data, it’s clear that TV viewers are highly motivated to watch a show if they hate something about it.

For dramas and reality shows, various expressions of “hate” by viewers were the strongest emotional indicator that TV show viewership will increase for the following episode, according to a study by data-analytics startup Canvs. The company looked at nearly 6,000 episodes of recent cable and broadcast shows, cross-referencing Nielsen ratings with Twitter comments associated with each show categorized into one of 56 emotional buckets (like “love,” “hate,” “annoying,” “beautiful” and “boring”).

A key finding: For every percentage point increase in “hate” responses, there’s a 0.7% increase in viewership for the next segment of a show in reality and drama genres. That’s more than double the power of “love,” for which a 1% increase corresponded to a 0.3% viewership boost for dramas and 0.2% uptick for reality shows.

“If you can’t stand a Kardashian, you are more likely to watch the show next week,” said Canvs founder and CEO Jared Feldman. “‘Hate’ is largely seen as negative — every network wants to reduce their negative scores on social media — but this study flips that logic on its head. You actually need ‘hate.'”

Of course, this isn’t really a shock: Villains have been integral to human storytelling for thousands of years. But the study shows the specific magnitude that feelings of hate have in drawing an audience into a plot.

For comedies, rather than “funny,” Canvs found “love” and “beautiful” to be the biggest emotional indicators of whether viewership will increase for the next episode. For every 1% increase in “love,” there’s a 0.1% increase in viewership the next episode — but a 1% increase in “beautiful,” yields a 0.3% increase in viewership the next episode.

“On comedies we were very surprised ‘funny’ wasn’t there. But it’s par for the course. It’s part of every comedy,” Feldman said. Interestingly, the Canvs study found a 1% rise in “funny” reactions for drama series actually produced a 0.3% viewing increase.

Feldman suggested that TV producers will be able to use the emotional-response data to glean insights into which storylines and characters achieve maximum viewer lift, or let networks identify elements to feature in on-air promos and social-media tune-in campaigns.

For the study, Canvs analyzed 5,709 episodes of 431 comedy, reality and drama series that aired between January 2014 and June 2015. Of those, 29% were new shows and 71% were returning shows. Broadcast networks comprised 40% of the episodes studied, while basic cable made up 55% and premium cablers like HBO and Showtime represented 5%. Because the study relied on Nielsen data, it excluded shows on Internet platforms like Netflix, Hulu and Amazon Prime Video (which are not rated by Nielsen).

The predictions aren’t guarantees that TV viewership will go up or down — Feldman compares the data analysis to a weather forecast. But Canvs’ predictive methodology was accurate to within three percentage points over the time period surveyed, according to the company. Some in the industry may question the accuracy of the underlying Nielsen TV panel-based ratings (which have been beset by glitches in the past) but whatever their shortcomings they remain the principal measure of television audiences today. Asked about the statistical validity of the study, Canvs chief scientist Sam Hui said in a statement, “The sampling error for Nielsen is non-systematic and random — they are already incorporated in the error terms in our model, while the effects that we identified are systematic and predictable.”

Canvs uses thousands of different words, phrases, emoji and slang terms to identify emotional responses to TV on social media; in fact, 65% of those are not words found in a dictionary.

Using the same methodology, the company is launching a product called Canvs Viewership Probability, which will provide scores estimating whether viewership will go up or down for a given show’s next episode. Initially, the company will make the CVP research available to TV execs for their own networks, but it plans to roll out syndicated research in the next few months (which Feldman said will be more of interest to ad agencies). In addition, Canvs is working with Facebook directly to compile research for TV viewing based on its data.

Feldman founded Canvs using social-media analytics research from Hui, previously a professor at NYU’s Stern School of Business and currently an associate professor at the University of Houston’s Bauer College of Business. The New York-based company recently hired Justin Fromm, previously with Hulu and ABC, as director of research.

Canvs customers include Viacom, Sony Pictures Television, NBCUniversal, CAA and UTA. Investors include KEC Ventures, Rubicon Ventures, BRaVe Ventures, Social Starts and Milestone Venture Partners.

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