The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

by   Jon Kleinberg, et al.

Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in user models. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want: we can choose mindlessly or myopically, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider what happens when a platform that simply wants to maximize user utility is only able to observe behavioral data in the form of user engagement. Our model produces phenomena related to overconsumption that are familiar from everyday experience, but difficult to capture in traditional user interaction models. A key ingredient is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. We show how the relationship between engagement and utility depends on the structure of the content manifold, characterizing when engagement optimization leads to good utility outcomes. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media.


page 10

page 11

page 16


Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor

Social media platforms moderate content for each user by incorporating t...

Using Interaction Data to Predict Engagement with Interactive Media

Media is evolving from traditional linear narratives to personalised exp...

Characterizing and Predicting Supply-side Engagement on Crowd-contributed Video Sharing Platforms

Video sharing and entertainment websites have rapidly grown in popularit...

"So, Tell Me What Users Want, What They Really, Really Want!"

Equating users' true needs and desires with behavioural measures of 'eng...

Chatbots language design: the influence of language variation on user experience

Chatbots are often designed to mimic social roles attributed to humans. ...

An interview method for engagement with personal data

Whether investigating research questions or designing systems, many rese...

What is the Will of the People? Moderation Preferences for Misinformation

To reduce the spread of misinformation, social media platforms may take ...

Please sign up or login with your details

Forgot password? Click here to reset