Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries
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Abstract (arXiv)
While research continues to investigate and improve the accuracy, fairness, and normative appropriateness of content moderation processes on large social media platforms, even the best process cannot be effective if users reject its authority as illegitimate. We present a survey experiment comparing the perceived institutional legitimacy of four popular content moderation processes. We conducted a within-subjects experiment in which we showed US Facebook users moderation decisions and randomized the description of whether those decisions were made by paid contractors, algorithms, expert panels, or juries of users. Prior work suggests that juries will have the highest perceived legitimacy due to the benefits of judicial independence and democratic representation. However, expert panels had greater perceived legitimacy than algorithms or juries. Moreover, outcome alignment - agreement with the decision - played a larger role than process in determining perceived legitimacy. These results suggest benefits to incorporating expert oversight in content moderation and underscore that any process will face legitimacy challenges derived from disagreement about outcomes.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2022.
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Citing papers per year: 2023: 1; 2024: 5; 2025: 1.
7 papers.
- 2024Content Moderation by LLM: From Accuracy to Legitimacycited by 30
- 2024Plurals: A System for Guiding LLMs Via Simulated Social Ensemblescited by 17
- 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
- 2024DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorshipcited by 6
- 2023Enhancing Content Moderation with Culturally-Aware Modelscited by 3
- 2024ARTiST: Automated Text Simplification for Task Guidance in Augmented Realitycited by 0
- 2025PolicyPad: Collaborative Prototyping of LLM Policiescited by 0
- 2025PolicyPad: Collaborative Prototyping of LLM Policies24 Sep 2025
- 2024DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship4 Nov 2024
- 2024Plurals: A System for Guiding LLMs Via Simulated Social Ensembles25 Sep 2024
- 2024Content Moderation by LLM: From Accuracy to Legitimacy5 Sep 2024
- 2024ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality29 Feb 2024
- 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases19 Jan 2024
- 2023Enhancing Content Moderation with Culturally-Aware Models5 Dec 2023