How Pinterest powers a healthy comment ecosystem with machine learning

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As Pinterest continues to evolve from a place to just save ideas to a platform for discovering content that inspires action, there’s been an increase in native content from creators publishing directly to Pinterest. With the creator ecosystem on Pinterest growing, we’re committed to ensuring Pinterest remains a positive and inspiring environment through initiatives like the Creator Code, a content policy that enforces the acceptance of guidelines (such as “be kind” and “check facts”) before creators can publish Idea Pins. We also have guardrails in place on Idea Pin comments including positivity reminders, tools for comment removal and keyword filtering, and spam prevention signals. On the technical side, we use cutting edge techniques in machine learning to identify and enforce against community policy-violating comments in near real-time. We also use these techniques to surface the most inspiring and highest quality comments first in order to bring a more productive experience and drive engagement.

Since machine learning solutions were introduced in March to automatically detect potentially policy-violating comments before they’re reported and take appropriate action, we’ve seen a 53% decline in comment report rates (user comment reports per 1 million comment impressions).

Here, we share how we built a scalable near-real time machine learning solution to identify policy-violating comments and rank comments by quality.


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