An explainable triage layer for Reddit mods — surfaces likely synthetic content, spam, scams, and coordinated inauthentic behavior with multi-signal scoring, confidence-based review tiers, verify-author appeals, and mod-collision prevention. Human judgment is preserved by default.
Other tools ask: "Is this AI?" Slopguard asks: "What should mods do with this suspicious item, and how do we handle it fairly?"
That's the whole product.
Slopguard is a Devvit-native moderation tool that scores incoming posts and comments using six local-first signals (no HTTP, no LLM required for the core product) and routes items into confidence tiers. Each flag comes with full per-signal explainability — never a black box.
It covers AI-generated content, karma-farm spam, affiliate/promo accounts, scam patterns, and dupe-content bots from the same multi-signal architecture. Local signals run in milliseconds at zero cost. An optional LLM-ensemble escalation tier (Gemini 2.5 Flash + Claude Haiku 4.5 + GPT-4o-mini) is available for mods who want a tiebreaker on uncertain cases.
| Confidence | Default action (Advisory mode) | Strict mode (opt-in) |
|---|---|---|
| 92%+ | Surface to mod queue with full explainability panel | Auto-remove (mod-configured) |
| 75–92% | Verify-author: auto-modmail asks author to explain; reply attaches to the review card | Same |
| 50–75% | Flag to mod queue with explainability | Same |
| < 50% | Ignore (not surfaced) | Same |
All thresholds + policy modes are per-sub configurable. Default is Advisory — Slopguard never removes anything by itself unless the mod team explicitly opts into Strict. Configure once; only see what needs attention.
How it's different from Stop AI
Stop AI is the incumbent in this space (431 communities, detection-first via Binoculars/DetectGPT/GLTR/TypeTruth). Slopguard is workflow-first: confidence bands, review coordination, author verification, and reversible decisions. Works standalone or alongside Stop AI as the workflow + fairness + collision-safety layer.
| Stop AI | Slopguard | |
|---|---|---|
| Approach | Detection-first (academic algorithms produce a score) | Workflow-first (multi-signal triage with explainable tiers) |
| Threshold | Fixed 50% binary | Graduated tiers, configurable per-sub |
| Default mode | Detection + optional autopilot enforcement | Advisory — surface to queue, mod decides |
| False-positive handling | Mod adjusts threshold | Verify-author modmail before action — fair to non-native English |
| Explainability | Score from algorithm | Per-signal panel ("em-dash density 3.2/1k chars, 1-day account, duplicate body hash, telegram contact") |
| Mod-collision prevention | None | Real-time "u/X is reviewing this" lock |
| Policy modes | One | Three (Advisory / Verify / Strict) |
| Signal | What it catches | Cost |
|---|---|---|
| Structural | AI-output patterns — em-dash density, formulaic phrases, sentence-length burstiness, markdown heading patterns. Capped at 0.2 unless ≥2 indicators agree (non-native-English protection). | Free, ~5ms |
| Behavioral | Account / submission metadata where exposed by Devvit — new accounts, low karma, cross-posting velocity, posting cadence. | Free, ~10ms |
| Promo | Affiliate links, URL shorteners, "DM me for…" patterns, crypto wallet addresses, common pump phrases. | Free, ~5ms |
| Contact-leak | Phone numbers (with strong-shape filter), Telegram handles (only with context), WhatsApp links, personal emails. | Free, ~5ms |
| Duplication | Hash match against prior submissions in the same sub within a 48h window — catches AI mass-posting AND copy-paste spam. | Free, ~10ms |
| History | User prior-flag count + confirmed-flag rate, with time decay. | Free, ~5ms |
| LLM ensemble (opt-in escalation) | Gemini 2.5 Flash + Claude Haiku 4.5 + GPT-4o-mini, weighted fusion, disagreement signal. Only triggers on gray-band local scores. | ~$0.0004/item, ~800ms |
The orchestrator weights signals and applies a corroboration boost (single strong signal alone is not enough — multiple firing signals are needed before the combined score is amplified above the weighted average). When LLM escalation runs, the local and LLM scores are fused with local-leaning weight; the ratchet up only fires when both sides agree above 0.5.
- Per-signal explainability on every flag — never a black box
- Verify-author workflow — auto-modmail asks the user to explain; reply attaches to the review card. Research-backed via AppealMod (arxiv 2301.07163).
- Mod-collision lock — "u/X is reviewing this" prevents two mods acting on the same item. Solves the CHI 2026 documented 74.5% collision pain directly.
- Per-sub policy modes — Advisory (default) / Verify / Strict
- Mod menu actions — "Analyze this post" (manual triage), "Claim review" (collision lock), "Explain score" (full breakdown), "Remove as AI slop" (single-click remove + reason + reply + modnote)
- Daily metrics post — items scored, flagged, removed, time saved
- Modnote integration for cross-mod consistency
- Mobile parity via Devvit — works in Reddit's mobile app
No AI detector is perfectly accurate. Stanford documented a 61.22% false-positive rate on TOEFL essays. Fixed-threshold autopilot without an appeals path can silently penalize innocent users — especially non-native English speakers.
Slopguard is built around this reality:
- Default mode is Advisory — never removes by itself unless Strict mode is explicitly enabled
- LLM ensemble is opt-in escalation, not core
- Structural signal is capped at 0.2 unless ≥2 strong indicators agree, protecting non-native writers
- Multi-model disagreement is surfaced explicitly when LLM is enabled
- Verify-author safety net handles the 75–92% band fairly
- Every action is reversible and logged
- Mod judgment is the final authority on any ambiguous case
- Visit developers.reddit.com/apps/slopguard
- Click Install → pick your subreddit
- Open the app's Settings:
- Set Policy mode (Advisory recommended for first 7 days)
- Optional: paste a Gemini API key (free tier: Google AI Studio) and toggle "Escalate uncertain cases to LLM"
- Done. The default is local-only, free, and flag-only.
Optional: add Anthropic + OpenAI keys for the full three-model escalation ensemble.
- Local-only mode (default): $0/month at any volume — runs entirely on Devvit Redis + signals
- With LLM escalation:
- Gemini Flash free tier (~1,500 free requests/day) covers most small/medium subs at $0/month
- Paid escalation: ~$0.0004 per item scored
- Cost discipline: karma threshold gating, approved-user skip, account-age skip, daily spend cap, mod skip
PostCreate / CommentSubmit
│
▼
runTriage (local-first)
│
├─ structural ─┐
├─ behavioral ─┤
├─ duplication ┤ weighted fusion
├─ history ─┤ + corroboration boost
├─ promo ─┤
└─ contact ──┘
│
▼ combined score in gray band [0.4, 0.75]?
│ │
│ │ yes ─► scoreItem (LLM ensemble) ─► fuse(local, llm)
│ │
▼ ▼
final score + topReasons → saveScore (Redis) → policy action
│
├─ Advisory: flag only
├─ Verify: flag + modmail to author
└─ Strict: flag + modmail + auto-remove (if ≥ threshold)
See ARCHITECTURE.md for full details and PRODUCT.md for the canonical product description.
- CHI 2026 mod collision study — 74.5% of mods experience modqueue collisions (Bajpai & Chandrasekharan, n=110, arxiv 2509.07314)
- Stanford AI-detector false positives — 61.22% on TOEFL essays (Liang et al, Patterns 2023, Stanford HAI)
- AppealMod — research-backed friction-on-appeal reduces mod load while preserving fairness (arxiv 2301.07163)
- r/programming temporary AI ban — April 2026 (Tom's Hardware)
- LLM-aided reply parsing for the verify-author workflow (auto-classify legitimate vs. evasive responses)
- Image AI detection via Gemini vision
- OCR for text-in-images (hidden promo/contact info)
- Cross-sub federation — opt-in shared bad-actor lists across participating subs (privacy framework first)
The Reddit Mod Tools & Migrated Apps Hackathon (2026), closing 2026-05-27.
Shifat Islam Santo, UTD CS '27 — u/shifatsanto75
MIT — see LICENSE.