AI-Assisted Hybrid Moderation Framework

• AI-Assisted Hybrid Moderation Framework is a system that integrates AI-generated feedback with community fact-checking to enhance the quality and balance of political content.
• The framework operates through a three-stage process: initial human drafting, AI feedback generation in argumentative, supportive, or neutral forms, and a subsequent revision stage.
• Empirical findings indicate that higher engagement with AI feedback—especially argumentative inputs—significantly improves note quality and mitigates partisan bias.

An AI-assisted hybrid moderation framework is an integrative system that leverages machine learning and generative AI to augment community-based content moderation, specifically by embedding AI-generated feedback—supportive, neutral, or argumentative—into the content revision process. This model aims to harness the strengths of both automated systems and human collective intelligence to improve the quality and balance of fact-checking contributions on politically charged social media platforms (Mohammadi et al., 10 Jul 2025).

1. System Architecture and Workflow

The architecture is centered around a human–AI collaboration model within community-driven fact-checking applications, such as Community Notes on X. The moderation pipeline follows three core stages:

1. Initial Contribution: Human participants draft a fact-checking note on a social media post, mimicking the workflow of platforms such as Community Notes.
2. AI Feedback Generation: The draft note is submitted to a LLM (e.g., GPT-4), which produces feedback in one of three forms:
   • Argumentative: Presents counterarguments or dissenting perspectives.
   • Supportive: Reinforces existing points or offers clarification.
   • Neutral: Restates content in a slightly altered form, without additional evaluation.
3. Revision: The human author is prompted to revise the original note in response to the feedback, with the option to incorporate suggested changes or disregard them.

A key operational metric, called the Feedback Acceptance rate (FA), is used to measure the degree of engagement with the AI feedback. FA is formally defined as the cosine similarity between the vectorized representations of the feedback and the revised note, capturing how much textual overlap results from the interaction.

2. AI Feedback Mechanism and its Role

The AI feedback mechanism strategically introduces diversity and engagement into the moderation process:

• Argumentative Feedback: By supplying counterpoints or critical perspectives, this feedback type stimulates users to confront opposing arguments, encouraging them to expand justification and add nuance to their revisions.
• Supportive Feedback: This mode buttresses the original argument, prompting users to elaborate or clarify their reasoning, typically resulting in incremental improvement.
• Neutral Feedback: Mainly leads to surface-level changes by paraphrasing or reordering existing information.

The system randomly assigns the purported source of feedback (human expert or AI agent) to participants, allowing the paper of how perceived source influences feedback engagement—though, operationally, all feedback is AI-generated (Mohammadi et al., 10 Jul 2025).

3. Empirical Impact on Moderation Quality

Quantitative evaluation demonstrates that incorporating AI-generated feedback yields measurable improvements in note quality:

• Helpfulness Ratings: Revisions are assessed using crowdsourced ratings from self-identified Democrats and Republicans on a standardized 0–10 scale, then normalized per rater and post as follows:

$$\hat{H}_{u,i}^{T} = \frac{H_{u,i}^{T} - \mu_{uT}}{\sigma_{uT}}$$

where $H_{u,i}^{T}$ is the helpfulness rating by rater $u$ for note $i$ on post $T$, and $\mu_{uT}, \sigma_{uT}$ are rater-specific statistics.

• Improvement Metric: A revision is counted as improved if its rating increases by more than 10%.
• Feedback Acceptance Association: Statistical modeling (ordinal logistic regression) reveals that higher FA scores—indicating closer integration of feedback—are robustly associated with improved ratings for both partisan groups.

The strongest gains in content helpfulness are observed when users engage with argumentative feedback, which provides direct counterarguments. Engagement with supportive and neutral feedback produces smaller improvements, mainly limited to increased clarity or minor elaboration (Mohammadi et al., 10 Jul 2025).

4. Counterarguments and Collective Intelligence

Argumentative feedback is empirically shown to have the greatest impact on the quality of moderated content. By directly exposing participants to counter-perspectives, the framework encourages reflection and critical evaluation of one’s own arguments.

• Critical Reflection: Users engaging with counterarguments are more likely to critically assess their initial statements, leading to more balanced and comprehensive revisions.
• Statistical Effect: Incorporating argumentative feedback, as measured by the interaction of feedback type and FA, yields larger odds ratios of note improvement compared to other feedback types.

This mechanism operationalizes collective intelligence by facilitating the synthesis of diverse views within a structured, scalable moderation workflow (Mohammadi et al., 10 Jul 2025).

5. Challenges: Partisan Bias and Delays

Two principal challenges emerge in the hybrid system:

• Partisan (Co-Partisan) Bias: Participants are less likely to incorporate AI feedback into notes when the post aligns with their own ideology, potentially limiting improvements in cases of ideological congruence.
• Delays in Moderation: Adjustments intended to reduce partisan effects—such as requiring helpulness ratings from ideologically diverse raters—inadvertently introduce verification delays, reducing the system’s responsiveness.

The framework addresses these by (i) leveraging counterargument feedback to simulate political diversity and trigger reflective revision, and (ii) preserving human agency by situating the final revision under participant control, thus mitigating automation bias (Mohammadi et al., 10 Jul 2025).

6. Implications for Political Content Moderation

This hybrid framework advances the moderation of politically sensitive content by:

• Enhancing Balance: AI-injected counterarguments reduce risk of echo-chambers and increase the informational value of fact-checks.
• Scalability: Automated feedback reduces the burden on expert fact-checkers, facilitating large-scale deployment.
• Mitigating Bias: By nudging participants to address dissenting views, the system actively counteracts partisan distortions in content moderation (Mohammadi et al., 10 Jul 2025).

This approach contributes new insights on augmenting human–AI collective intelligence in high-volume political moderation contexts.

7. Design Considerations for Generative AI Integration

The framework’s design emphasizes:

• Human Agency: AI functions solely as a source of suggestions, with humans empowered to reject or partly incorporate feedback.
• Active Engagement: The Feedback Acceptance metric provides an operational means of both quantifying and incentivizing substantive engagement with AI feedback, especially argumentative variants.
• Transparency and Source Labeling: By varying the ostensible source between human experts and AI, the system explores response to automation and optimizes labeling practices to maximize trust and receptiveness.

The results underscore the need for informed, human-centric system design when deploying generative AI feedback in community-based moderation workflows (Mohammadi et al., 10 Jul 2025).