Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers

2019-10-21

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7

Volume

Vol. 7 (2019): Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing

Technical Papers

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, the need for manual reviews still remains. 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. 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.

10.1609/hcomp.v7i1.5270