Preventing scrollers’ remorse: How to know what users want

ITHACA, N.Y. – Researchers have created a new model that can help online media companies figure out what gives users long-term satisfaction – not just the instant gratification of continual scrolling – which may result in less time spent on the platform, but fewer users who quit entirely.

Most online platforms seek to increase the time users spend there, usually by giving them more of the content they have consumed in the past. But this strategy can perpetuate mindless scrolling, and potentially cause regretful users to quit cold turkey. 

“There’s a discussion in the research community and in tech companies about how it can be that people use online media a lot, but often come away not valuing the time they spent,” said Jon Kleinberg, professor of computer science. Kleinberg co-authored a new paper that provides tools to help alleviate this conflict by giving online media companies new ways to figure out what users really want.

“These platforms are designed to watch what you do, and then give you more of what you want,” Kleinberg said. “So, on the one hand, these platforms are highly optimized. On the other hand, we often feel like we don’t make good choices when we’re on them. So how do we reconcile these two things?”

This inconsistency may be the result of two known facets of human decision making: system 1 and system 2. System 1 makes fast, almost automatic decisions, while system 2 is slower, reflexive and more logical. With food, system 1 wants the entire bag of chips, while system 2 chooses the salad. Both foods can be part of a balanced diet, but the chips provide gratification in the moment, while the salad provides long-lasting satisfaction. With online media, celebrity posts might trigger system 1, while an educational video might interest system 2. 

The researchers developed a model that simulates how a user with conflicting desires interacts with a platform, then suggests ways to prioritize the value the user receives. The new model is a starting point for companies to understand what drives user decisions. The model can help companies classify content as chips or salad, and to change the algorithm to prevent users from binging. 

Additionally, the model can suggest design changes. For example, platforms can let system 2 step in periodically by adding regular breaks, an option that some social media companies already provide. They can also disable autoplay, which tends to feed system 1’s impulsive decisions.

The authors are working with platform designers to find out which interventions successfully improve user happiness. They also aim to incorporate interactions between users into the model, to see how likes and comments from peers impact the experience.

For more information, see this Cornell Chronicle story.

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