Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
Keywords: content creator incentives, equilibrium characterization, societal impacts of online platforms
TL;DR: To investigate how engagement-based optimization by online platforms shapes the content landscape, we analyze a game between content creators who invest in content quality and utilize gaming tricks to win recommendations.
Abstract: Online content platforms commonly use engagement-based optimization when making recommendations. This encourages content creators to invest in quality, but also rewards gaming tricks such as clickbait. To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming. First, we show the content created at equilibrium exhibits a *positive correlation between quality and gaming*, and we empirically validate this finding on a Twitter dataset. Using the equilibrium structure of the content landscape, we then examine the downstream performance of engagement-based optimization along two axes. Perhaps counterintuitively, the average quality of content consumed by users can decrease at equilibrium as gaming tricks become more costly for content creators to employ. Moreover, engagement-based optimization can perform worse in terms of user utility than a baseline with random recommendations. Altogether, our results highlight the need to consider content creator incentives when evaluating a platform's choice of optimization metric.
Track: Economics, Online Markets, and Human Computation
Submission Guidelines Scope: Yes
Submission Guidelines Blind: Yes
Submission Guidelines Format: Yes
Submission Guidelines Limit: Yes
Submission Guidelines Authorship: Yes
Student Author: Yes
Submission Number: 249
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