When Connections Depress Contributions: The Hidden Cost of Social Filtering – MSI

Working Paper

When Connections Depress Contributions: The Hidden Cost of Social Filtering

Ziwei Cong, Jia Liu and Shijie Han

Dec 30, 2025

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Uses a quasi-experiment to investigate the impact of a social filtering algorithm on the volume, efficiency, quality and diversity of user-generated content on a knowledge-sharing platform.

Topics

Consumer Behavior & Insights Omnichannels & Customer Journey Social Media & Social Influencers

research recap

Curation v. Creation: The Pitfalls of Social Filtering

Recommendation algorithms can help consumers find useful information. Prioritizing content from a user’s social connections can strengthen engagement on online platforms, but such social filtering may also erode the volume, efficiency, quality and diversity of user-generated content. Platforms need to carefully balance homophily-driven recommendations with mechanisms that promote diverse interactions.

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