Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators

By Eshwar Chandrasekharan, Chaitrali Gandhi, Matthew Wortley Mustelier, Eric Gilbert. 2019

207 citations recorded by the source corpus.

In this paper, we introduce a novel sociotechnical moderation system for Reddit called Crossmod. Through formative interviews with 11 active moderators from 10 different subreddits, we learned about the limitations of currently available automated tools, and how a new system could extend their capabilities. Developed out of these interviews, Crossmod makes its decisions based on cross-community learning---an approach that leverages a large corpus of previous moderator decisions via an ensemble of classifiers. Finally, we deployed Crossmod in a controlled environment, simulating real-time conversations from two large subreddits with over 10M subscribers each. To evaluate Crossmod's moderation recommendations, 4 moderators reviewed comments scored by Crossmod that had been drawn randomly from existing threads. Crossmod achieved an overall accuracy of 86% when detecting comments that would be removed by moderators, with high recall (over 87.5%). Additionally, moderators reported that they would have removed 95.3% of the comments flagged by Crossmod; however, 98.3% of these comments were still online at the time of this writing (i.e., not removed by the current moderation system). To the best of our knowledge, Crossmod is the first open source, AI-backed sociotechnical moderation system to be designed using participatory methods.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Social Biases in NLP Models as Barriers for Persons with Disabilities (2020, background): “In a deployment context that involves human moderation, model scores may sometimes be used to select and prioritize messages for review by moderators (Veglis, 2014; Chandrasekharan et al., 2019).”
The Pushshift Reddit Dataset (2020, background): “2019; Jhaver, Bruckman, and Gilbert 2019), and improving automated moderation tools (Chandrasekharan et al. 2019).”
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice (2023, background): “(2019) [47] Academia Online Moderation Automate decisions for content moderation Prototype Evaluation,”
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “For example, the level of uncertainty could help determine when and how moderators take part in a human-in-the-loop content moderation (Chandrasekharan et al., 2019; Liu, 2020).”