Google Research

Testing Stylistic Interventions to Reduce Emotional Impact in Content Moderation Workers

AAAI Conference on Human Computation and Crowdsourcing 2019 (2019), pp. 50-58

Abstract

With the rise in user generated content, there is a greater need for content reviews. While machines and technology play a critical role in content moderation, there is still a need for manual reviews. It is known that such manual reviews could be emotionally challenging. We test the effects of simple interventions like grayscaling and blurring to reduce the emotional impact of such reviews. We demonstrate this by bringing in interventions in a live content review setup thus allowing us to maximize external validity. We use a pre-test post-test experiment design and measure review quality, average handling time and emotional affect using the PANAS Scale to measure emotional affect. We find that simple grayscale transformations can provide an easy to implement and use solution that can significantly change the emotional impact of content reviews. We observe however that a full blur intervention can be challenging to reviewers.

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