ModQ: Rule-Conditioned Moderation QA

• ModQ is a question-answering framework that conditions content moderation on the full set of active community rules, enabling rule-sensitive inference.
• It implements two variants—extractive QA and multiple-choice QA—to balance interpretability with empirical performance in predicting rule infractions.
• Empirical results on Reddit and Lemmy datasets show that ModQ outperforms state-of-the-art baselines and adapts robustly to evolving rule inventories.

Searching arXiv for the ModQ paper and closely related moderation-QA work to ground the article in current literature. Searching arXiv for "Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation" and "rule-sensitive content moderation question answering". ModQ is a question-answering framework for rule-sensitive content moderation that models moderation as the task of identifying which community rule best justifies moderation of a comment, given the comment and the full set of community rules in effect at moderation time. Introduced in "Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation" (Samory et al., 7 Oct 2025), it departs from binary moderation classification and coarse rule-category prediction by conditioning on the complete textual ruleset at inference time. The framework was implemented in two variants—extractive QA and multiple-choice QA—and evaluated on large-scale Reddit and Lemmy data, where it was reported to outperform state-of-the-art baselines in identifying moderation-relevant rule violations while remaining lightweight and interpretable (Samory et al., 7 Oct 2025).

1. Problem formulation and conceptual basis

ModQ is designed for online communities whose governance depends on a combination of platform policies and community-authored rules. In that setting, rules vary across communities, evolve over time, and may be enforced inconsistently. The central modeling decision is therefore to treat moderation as a rule-conditioned inference problem rather than as a fixed-label classification problem (Samory et al., 7 Oct 2025).

The framework takes as input a comment and the actual set of community rules present at moderation time, then predicts the specific rule that best applies. This rule-selection formulation is distinct from conventional pipelines that map a comment directly to a binary remove/keep decision or to a coarse taxonomy such as spam or hate. It is also distinct from generation-based moderation approaches: the paper explicitly positions ModQ against classification and generative methods, arguing that the QA formulation preserves rule sensitivity while avoiding the computational cost and reduced interpretability associated with larger generative systems (Samory et al., 7 Oct 2025).

A key consequence of conditioning on the full ruleset at inference time is that ModQ can operate across communities with different rule inventories without retraining for each new ruleset. This supports the paper’s emphasis on unseen communities, unseen rules, low-resource moderation, and dynamic governance environments. A plausible implication is that ModQ treats rule text as first-class supervision rather than as metadata attached to a closed label set.

2. QA formulations and model variants

ModQ was instantiated in two complementary forms: ModQ-Extract and ModQ-Select (Samory et al., 7 Oct 2025).

ModQ-Extract frames moderation as extractive QA. The input is the concatenation of the comment and the full ruleset, where rules are formatted as '{rule_n}. {rule_text}'. The model predicts a start and end token span in the rule context, and the predicted span is then mapped back to a rule by containment. The architecture is a fine-tuned BERT-based extractive QA model, specifically conversational-bert-base-cased. The loss is the sum of start-index and end-index cross-entropies:

$$\mathcal{L} = \text{CrossEntropy}(p_{\text{start}}, \hat{p}_{\text{start}}) + \text{CrossEntropy}(p_{\text{end}}, \hat{p}_{\text{end}})$$

ModQ-Select frames moderation as multiple-choice QA. For each comment and each rule in the candidate ruleset, the model evaluates whether that rule applies. The input for a comment–rule pair is

$\text{[CLS]}~c~\text{[SEP]}~r_i~\text{[SEP]}$

and the model computes a scalar applicability score

$s_i = f_{\theta}(c, r_i)$

with prediction by

$r^* = \underset{r_i}{\arg\max}~s_i.$

The underlying model is a fine-tuned BERT multiple-choice setup trained with binary applicability labels on comment–rule pairs (Samory et al., 7 Oct 2025).

These two designs expose a trade-off already implicit in the paper. ModQ-Extract yields explicit text spans, while ModQ-Select scores discrete rule candidates directly. The reported experiments indicate that the multiple-choice variant is generally stronger empirically, whereas the extractive variant preserves a more literal text-grounded rationale (Samory et al., 7 Oct 2025).

3. Datasets, supervision, and rule reconstruction

The empirical study used Reddit and Lemmy datasets, both organized around comments, moderation outcomes, and community rules. The Reddit data was drawn from a prior dataset of 20k threads containing a final moderated comment and a moderator-cited rule justification, and was augmented by reconstructing the full ruleset for each community. The Lemmy dataset was constructed from publicly available moderation logs and community rule descriptions (Samory et al., 7 Oct 2025).

Dataset	Communities	Coverage
Lemmy	413	40,534 modlogs; 21,518 safe comments; 19,016 removed comments
Reddit	2,264	50,732 modlogs; 31,119 safe comments; 19,613 removed comments

For Lemmy, the paper reports average rules per community of 6; for Reddit, average rules per comment of 3. Rules were also mapped to coarse categories for evaluation, including incivility, hate, spam, content, doxx, format, harassment, meta, off-topic, troll, other, and safe. The ModQ models themselves, however, operate on rule text rather than on these categories (Samory et al., 7 Oct 2025).

The Lemmy corpus required additional supervision construction. Rules were extracted via GPT-4o from free text and matched to moderation rationales by rule number or by semantic similarity using MiniLM embeddings with cosine similarity greater than 0.85. The paper also emphasizes that the rules are time-accurate, meaning that the ruleset is aligned with the community state at removal time (Samory et al., 7 Oct 2025). This time alignment is important because the framework’s stated goal is not merely to predict moderation outcomes, but to model enforcement relative to the rule inventory actually in force.

4. Training procedure and augmentation

The train/dev/test split was 80/10/10, stratified by community and moderation status. Training used 5 epochs, a learning rate of $1 \times 10^{-5}$, and weight decay of $0.001$, with experiments run on NVIDIA A100 or RTX 3060 GPUs (Samory et al., 7 Oct 2025).

For ModQ-Extract, the paper describes several augmentation procedures intended to prevent shortcut learning from rule ordering or numbering. These were rule shuffling, number permutation, and rule exclusion. Rule shuffling randomizes rule order; number permutation randomizes rule numbers; rule exclusion randomly drops one rule, sometimes the correct one. The stated purpose is to improve robust generalization and prevent memorization of ruleset structure rather than comment–rule semantics (Samory et al., 7 Oct 2025).

The evaluation protocol includes two out-of-domain settings in addition to standard splits: leave-$N$-communities-out and leave-$N$-rules-out. The former tests transfer to unseen communities; the latter tests transfer to previously unseen rules. This is integral to the paper’s claim that conditioning on the current ruleset allows moderation systems to adapt to evolving governance without task-specific retraining (Samory et al., 7 Oct 2025).

5. Empirical performance and out-of-domain generalization

The primary evaluation metric is macro F1 over rule categories plus the safe class, computed in a binary one-vs-rest setting for each category. On this measure, ModQ-Select is reported to consistently outperform all baselines, including NormVio and CPL-NoViD, across rule categories and moderation tasks. ModQ-Extract performs comparably to state-of-the-art systems despite addressing a finer-grained per-rule task rather than only per-category prediction (Samory et al., 7 Oct 2025).

Representative category-level results illustrate the pattern. On Lemmy, ModQ-Select reaches 0.87 for incivility, 0.90 for hate, 0.84 for spam, 0.89 for content, 0.91 for harassment, and 0.87 for safe. On Reddit, it reaches 0.86 for incivility, 0.81 for hate, 0.86 for spam, 0.79 for content, 0.89 for harassment, and 0.86 for safe. The extractive variant is close but generally lower, with 0.85/0.88/0.82/0.86/0.88/0.85 on the corresponding Lemmy categories and 0.85/0.82/0.85/0.77/0.87/0.83 on Reddit (Samory et al., 7 Oct 2025).

Model	Lemmy Safe F1	Lemmy Not Safe F1
NormVio	0.85	0.83
ModQ-Extract	0.86	0.84
ModQ-Select	0.87	0.86

The paper also reports that both ModQ variants outperform state-of-the-art models on unseen communities and unseen rules. This is one of the most consequential findings for deployment: the framework’s generalization target is not merely distribution shift in comments, but shift in the rule inventory itself. In that sense, ModQ is intended to be rule-adaptive rather than label-stable (Samory et al., 7 Oct 2025).

6. Interpretability, error structure, and practical significance

Interpretability is central to ModQ’s design. Because the prediction is an explicit rule or rule span, the output can be surfaced directly to moderators and users as a moderation rationale. ModQ-Extract produces a text span from the ruleset; ModQ-Select returns the specific rule index. The paper presents this as a practical advantage over moderation systems that only emit a class label or a remove/keep decision (Samory et al., 7 Oct 2025).

The framework is also described as lightweight and scalable. Both variants are based on moderate-sized BERT fine-tuning rather than large generative inference, and they require only the current ruleset as context at test time. This makes the system suitable, in the paper’s terms, for volunteer or low-resource moderation settings (Samory et al., 7 Oct 2025).

The reported error analysis is notable for what it does not claim. The most common confusions are said to be semantically reasonable, such as content versus format, or safe versus borderline rule categories. The paper further notes that the remaining difficult cases tend to involve contextual nuance or low training support. This suggests that ModQ does not eliminate ambiguity in moderation; rather, it relocates prediction into a rule-grounded space where ambiguous cases are more legible (Samory et al., 7 Oct 2025).

A common misconception is that ModQ is simply a more elaborate binary moderation classifier. The paper’s formulation contradicts that reading: the model is trained to identify which rule applies from the active ruleset, and category-level moderation performance is a downstream evaluation projection rather than the primary prediction target. Another common misconception is that rule-sensitive moderation necessarily requires large generative models. ModQ is explicitly presented as an alternative that is both efficient and interpretable while still outperforming prior systems on the reported benchmarks (Samory et al., 7 Oct 2025).

7. Role in moderation research and governance analysis

ModQ occupies a specific position in the moderation literature: it is a rule-conditioned, inference-time adaptive framework for community governance rather than a global policy classifier. Because it conditions on the live rule inventory, it can be used not only for enforcement support but also for governance analysis. The paper notes that the framework can support flagging systems, explanation interfaces, and analysis of the relationship between stated rules and actual moderator enforcement (Samory et al., 7 Oct 2025).

This suggests a broader research significance. By keeping rule text in the inference loop, ModQ provides a mechanism for studying discrepancies between normative documents and enforcement outcomes at scale. It also supports simulation of rule revisions, because changing the ruleset changes the moderation context without changing the model architecture. Within the limits of the reported experiments, this makes ModQ not only a predictive system but also an instrument for analyzing transparency, governance variation, and rule drift across communities (Samory et al., 7 Oct 2025).