Ranking algorithms play a central role in user-generated content (UGC) platforms by determining which content receives visibility and, consequently, influencing which creators receive recognition. This dissertation examines the consequences of replacing a popularity-based ranking algorithm for ordering content with a fairness-motivated ranking algorithm intended to reduce rank dependence on accumulated popularity on UGC platforms. Leveraging the release of a fairness-motivated ranking algorithm on Zhihu, the largest Chinese social question-and-answer platform, on December 5, 2014, this dissertation employs a regression discontinuity in time design to investigate how the algorithmic change affected the volume of content users create, the recognition and social ties creators receive (and how these outcomes vary across creators), and the informativeness of recognition signals. The results demonstrate an incentive effect on content production: questions received more answers, and content creators posted more answers per week after the algorithmic change. The results also reveal a heterogeneous effect on the benefits gained by creators: the intervention increased creators’ weekly upvote gains and follower growth, with stronger effects among shorter-tenured creators, suggesting that fairness-motivated ranking expanded opportunities for recognition and social growth among less established creators. However, the algorithmic change decreased the weekly bookmarks-to-upvotes ratio across content creators, indicating upvote inflation where upvotes increased faster than bookmarks and became less aligned with the more stable bookmark signal as a signal of content quality. These findings suggest that fairness-motivated ranking algorithms can broaden creator participation and expanding opportunities for recognition, but they may also cause recognition inflation by weakening the informativeness of recognition signals. This dissertation contributes to research on ranking algorithms, platform design, and UGC communities by showing that ranking algorithms function as structural mechanisms that shape creator incentives, competitive outcomes, and the informativeness of the input signals on which ranking systems themselves depend. For platform practitioners, this research offers critical, actionable insights for navigating the complex trade-offs between lowering barriers to entry for newcomers and preserving the integrity of user feedback systems.