Year: 2019
Type: article
Abstract: When posts are removed on a social media platform, users may or may not receive an explanation. What kinds of explanations are provided? Do those explanations matter? Using a sample of 32 million Reddit posts, we characterize the removal explanations that are provided to Redditors, and link them to measures of subsequent user behaviors---including future post submissions and future post removals. Adopting a topic modeling approach, we show that removal explanations often provide information that educate users about the social norms of the community, thereby (theoretically) preparing them to become a productive member. We build regression models that show evidence of removal explanations playing a role in future user activity. Most importantly, we show that offering explanations for content moderation reduces the odds of future post removals. Additionally, explanations provided by human moderators did not have a significant advantage over explanations provided by bots for reducing future post removals. We propose design solutions that can promote the efficient use of explanation mechanisms, reflecting on how automated moderation tools can contribute to this space. Overall, our findings suggest that removal explanations may be under-utilized in moderation practices, and it is potentially worthwhile for community managers to invest time and resources into providing them.
DOI: 10.1145/3359252
Authors: Shagun Jhaver, Amy Bruckman, Eric Gilbert
Institutions: Georgia Institute of Technology, University of Michigan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W2989364584