Using Data to Predict Virality with Ernan Haruvy Ph.D. of University of Texas at Dallas

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In the marketing class taught by Ernan Haruvy Ph.D. at the University of Texas at Dallas, “accelerants of relevance,” is the technical term students take notes on. For those without a marketing background, the term may be more easily understood as the data-supported elements of what makes a piece of social media content go viral.

We sat down with Haruvy on this episode of the Digital Marketing Professor Series to discuss the specific data analytics that his class uses to measure social media marketing effectiveness.

“We were trying to find out ‘what is it in terms of content and the actual demographics of the people doing tweets and retweets that accelerates retweeting that causes virality’,” Haruvy said.

As part of a collaboration with PR firm Golin, marketing students at University of Texas at Dallas analyzed tweets for content words, emotion, and sentiment analysis, including positivity and negativity. Haruvy said this is especially key in social media marketing.

“You don’t want something to just diffuse very fast; You want it to diffuse positively,” Haruvy said. “You want people to be excited about your product or story.”

On the podcast, Haruvy weighed in on whether a message from an unlikely source is either more, or less, likely to go viral. We even discussed Taylor Swift’s latest Instagram post, urging her fans to register to vote, which is the first outwardly political Instagram message she has posted.

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