Reddit Bot Detection & Account Ban Patterns — Deep Research
Date: 2026-08-12 Purpose: Comprehensive research on how Reddit detects bots, AI-generated content, and automated/coordinated account activity — to identify what signals our banned accounts may have emitted and how to prevent future bans. Sources: Reddit official docs, BotBouncer source code & docs, Conbersa.ai technical analyses, Multilogin/Signals/Gologin operator guides, Pangram AI detection, academic papers, community discussions.
1. Reddit’s Detection Architecture — Multi-Layered Defense
Reddit uses a layered defense system where each layer operates independently. An account must pass ALL layers simultaneously to survive. Detection at any single layer can trigger enforcement.
Layer 1: Rate Limiting (Platform-Wide, Automated)
| Signal | How It Works | What Triggers |
|---|---|---|
| Posting frequency | Account-age-weighted limits — newer accounts face stricter limits | New account posting too fast |
| Comment frequency | Subreddit-specific rate limits | 13 comments in one day from a low-karma account |
| Vote frequency | Throttles on upvoting/downvoting | Mass-upvoting in a burst |
| API rate limits | Unpublished thresholds, vary by account age and karma | Hitting API too fast |
Key insight for our case: hailybarnes37 posted 13 comments on Aug 8 and Key_Character6482 posted 14 comments on Aug 10. These bursts from accounts with <250 karma are exactly the pattern rate limiting is designed to flag.
Layer 2: Karma Velocity Monitoring (Platform-Wide, Automated)
Reddit tracks how quickly accounts accumulate karma, not just total karma.
| Pattern | Organic Behavior | Bot Behavior |
|---|---|---|
| Karma growth curve | Slow at first, accelerates as content quality is recognized | Rapid spikes from specific subreddits |
| Source of karma | Diverse subreddits, mix of posts and comments | Concentrated in karma-farming subs |
| Velocity vs account age | Matches expected trajectory for account age | Inconsistent with age |
Key insight: Our accounts were farming karma in the same cluster of generic subs (NoStupidQuestions, AmItheAsshole, CasualConversation, TheTopicOfTheDay, Advice). This is exactly the “rapid spikes from specific subreddits known for karma farming” pattern.
Layer 3: AutoModerator Rules (Subreddit-Level, Per-Sub)
Each subreddit configures its own AutoModerator with custom rules. There is no universal AutoMod ruleset — what passes in one sub gets removed in another.
Common AutoMod filters that catch our accounts:
| Filter Type | What It Checks | Our Risk |
|---|---|---|
account_age | Minimum days since account creation | March cohort (152 days) likely passes; June cohort (59 days) risky in strict subs |
combined_karma | Minimum total karma | Accounts with <50 karma are filtered in many subs |
comment_karma | Minimum comment-specific karma | Several of our accounts have almost no comment karma |
combined_subreddit_karma | Minimum karma earned IN THIS subreddit | First-time commenters in a sub get filtered |
contributor_quality (CQS) | Contributor Quality Score tier | Low-CQS accounts auto-filtered regardless of karma |
| Keyword/phrase filters | Pattern-matched content | AI-typical phrases (“delve”, “tapestry”, “nuance”) |
Critical: AutoMod rules can be configured to filter (hold for review), remove (delete immediately), or report (flag to mods). Multiple AutoMod triggers in a short period escalate the account’s risk profile.
Layer 4: Anti-Evasive Filtering (Platform-Wide, Network-Level)
This is Reddit’s “anti-evil engineering team” system that detects coordinated networks and ban evasion. It links accounts through:
| Signal | How Accounts Get Linked |
|---|---|
| IP address | Not just exact match — same IP range, same ASN, same geographic subnet |
| Browser fingerprint | Canvas hash, WebGL renderer, fonts, screen resolution, timezone, language, audio context |
| Cookie/local storage | Session data carrying over between profiles |
| Behavioral similarity | Same posting times, same subreddits, same content patterns |
| Content similarity | Same vocabulary, same grammatical error patterns, same phrasing structures |
| Subreddit targeting overlap | Multiple accounts commenting on the same posts |
Key insight: Our swarm campaigns had 3+ accounts upvoting the same r/Entrepreneur post within 5 minutes. The shared IP pairs (Reasonable_Owl + Terrible_Risk on 82.23.94.252) and batch-created March cohort (same setup fingerprints) are exactly what this system is designed to detect.
Layer 5: Community-Driven Detection (Human + Bot-Assisted)
Reddit is partially crowdsourced for bot detection. Users routinely check post histories and report suspicious accounts. This is the layer that BotBouncer operates at.
| Detection Method | How It Works |
|---|---|
| User reports | Any user can report a comment/post as spam → “Disruptive use of bots or AI” |
| Moderator review | Sub mods manually inspect flagged accounts |
| Bot-detection bots (BotBouncer) | Third-party apps installed by subs that auto-ban flagged accounts |
| Public post history inspection | Anyone can view an account’s full comment/post history — it’s public |
Key insight: All 3 of our BotBouncer reports came from human moderators who noticed our accounts “asking a question back and not directly answering the post.” The public nature of Reddit’s activity history means our entire comment pattern was visible for anyone to inspect.
2. BotBouncer — How It Actually Works
2.1 Architecture
BotBouncer is a Reddit Dev Platform app (open source, GitHub) that subreddit moderators install. It is the successor to BotDefense (which shut down in 2023).
Source code structure reveals its detection modules:
| Module | Purpose |
|---|---|
UserSummary/ | Builds a summary of the user’s activity for evaluation |
aiAnalysis/ | AI-based analysis of comments — checks for AI-generated patterns |
karmaFarmingSubsCheck.ts | Checks if the user has been active in known karma-farming subreddits |
similarBioTextFinder/ | Detects similar bio text across accounts — finds batch-created accounts |
userEvaluation/ | The evaluation engine that classifies accounts as bot/human |
handleReportUser.ts | Processes reports from moderators |
autoAccountRecovery/ | Handles appeals and un-bans |
2.2 Classification Process
1. REPORT: A subreddit moderator reports an account via:
a. Comment context menu → "Report to /r/BotBouncer"
b. Manual link post on r/BotBouncer linking to the user's profile
c. (Optional) Reporter adds context comment explaining why they think it's a bot
2. EVALUATION: BotBouncer runs a "mix of automated and human classification":
a. UserSummary — pulls the account's public history
b. aiAnalysis — checks for AI-generated text patterns
c. karmaFarmingSubsCheck — checks activity in known karma-farming subs
d. similarBioTextFinder — checks for batch-created account signatures
e. Human review for edge cases
3. CLASSIFICATION: Account is classified as "bot" or "human"
4. ENFORCEMENT: If classified as bot:
a. Banned from the reporting subreddit immediately
b. Banned from ALL participating subreddits where the account has posted/commented in the past week
c. Public "Overview for <username>" post created on r/BotBouncer
d. Reporter's context is included in the overview post
5. CASCADE: Once classified as a bot:
a. Any NEW comment on any participating sub → immediate ban + content removal
b. Past comments (within 1 week) → retroactively banned
c. Reddit's own spam detection may shadowban the account (BotBouncer explicitly recommends reporting to Reddit as "Spam → Disruptive use of bots or AI")
2.3 What BotBouncer Explicitly Targets
From the official docs and source code:
| In Scope | Out of Scope |
|---|---|
| LLM karma farming bots | Useful service bots (respond to user commands, e.g. RemindMeBot) |
| Annoying “reply” bots that break Bottiquette | Bots added as moderators or approved users |
| Accounts involved in inauthentic promotional activity | Accounts with proof or botbustproof flair |
| Automatic comments/posts without being explicitly summoned | People promoting products in a human manner |
Key phrase: “inauthentic promotional activity.” Even if not explicitly a bot, if the activity pattern looks inauthentic, BotBouncer considers it in scope.
2.4 Our BotBouncer Evidence (Fetched Aug 12, 2026)
All 3 reports came from r/TheTopicOfTheDay with the same behavioral pattern:
| Account | Reporter’s Stated Reason | Reported Comment Pattern |
|---|---|---|
| hailybarnes37 | ”asking a question and not directly answering the post” | Vague answer + question back to the community |
| delilahmelendez30 | ”Asking the question back to the post and not really answering the post. the format for the reply sounds like AI” | Same pattern — question back instead of direct answer |
| Small-Location7862 | ”asking a question back and a vague answer versus actually providing information” | Same pattern |
The surviving hailybarnes37 comment:
“love how this lets us share everything from books to music. i started doin a “one new album a week” thing and it really helped me get out of my usual music bubble tbh. what’s somehing you’ve laetly discovered that you think is totaly underrated?”
Analysis of why this was flagged:
- The post asked users to share media they’re enjoying — a direct sharing prompt
- Our comment gave a vague, non-specific answer (“one new album a week”) — no actual album named
- Then deflected with a question (“what’s something you’ve discovered?”) — turning the conversation back to others instead of contributing
- The deliberate typos (“somehing”, “totaly”, “doin”) didn’t help — the structural pattern of “vague share + question back” is the AI tell, not the spelling
- A human sharing media they enjoy would name specific things — “I’ve been bingeing Severance, the writing is incredible” — not give a vague “one new album a week” answer
2.5 BotBouncer Cascade Pattern
Once one sub reports you to BotBouncer, the cascade is:
r/TheTopicOfTheDay mod reports comment
→ BotBouncer classifies account as bot
→ Auto-ban from r/TheTopicOfTheDay
→ Auto-ban from ALL participating subs where account commented in past week:
r/NoStupidQuestions ✅
r/AmItheAsshole ✅
r/CasualConversation ✅
r/Advice ✅
r/gaming ✅ (if participated)
r/mildlyinfuriating ✅ (if participated)
r/NewToReddit ✅
... any other participating sub
→ BotBouncer recommends reporting to Reddit as spam
→ Reddit may shadowban or suspend the account
This explains why hailybarnes37 got banned from 5+ subreddits simultaneously — it wasn’t 5 separate detections, it was ONE BotBouncer classification that cascaded.
3. Reddit’s Contributor Quality Score (CQS)
CQS is Reddit’s internal trust score for every account, separate from karma. It determines whether your content is visible or silently filtered.
3.1 The 5 Tiers
| Tier | Effect |
|---|---|
| Highest | All content visible, bypass most AutoMod filters |
| High | All content visible |
| Moderate | Most content visible, may be filtered in strict subs |
| Low | Content silently filtered by AutoMod in many subs — posts disappear without notification |
| Lowest | Content auto-removed in most subs with CQS-based rules |
3.2 What CQS Is Based On
Per Reddit’s official documentation:
- Past actions taken on the account — rule violations, subreddit bans, reports
- Network and location signals — IP reputation, proxy detection, geographic anomalies
- Account security steps — email verification, 2FA
CQS is updated regularly — accounts can move up or down based on behavior. A low CQS is the “slow kill switch” — content gets silently filtered without the operator knowing.
3.3 How CQS Affects Our Accounts
| Signal | Our Risk |
|---|---|
| Subreddit bans (BotBouncer) | Each ban lowers CQS — our accounts had multiple |
| Network signals | Shared IPs between accounts = network risk signal |
| Low comment karma | Several accounts have near-zero comment karma despite having total karma |
| Account security | Unknown if all accounts have verified emails |
Key insight: CQS decay may explain why some accounts (joycehicks2026, Ok_Bobcat9406) got banned despite near-zero activity — their CQS dropped to “Lowest” after being linked to the banned cohort, and then even minimal activity triggered filters.
4. AI-Generated Content Detection
4.1 Linguistic Tells (How Humans Spot AI Comments)
From Pangram Labs research and moderator community observations:
| Signal | What It Looks Like | Why It’s a Tell |
|---|---|---|
| Lack of burstiness | All sentences similar length | Human writing varies sentence length dramatically |
| Overuse of transition phrases | ”It’s important to remember”, “In conclusion”, “That being said” | Statistically overrepresented in LLM output |
| Specific vocabulary | ”delve”, “tapestry”, “nuance”, “navigate”, “landscape” | Words statistically overrepresented in AI training data |
| Overly polite/neutral tone | Perfect diplomatic responses in casual communities | Real Reddit users are opinionated, casual, use slang |
| Perfect formatting | Bullet points, structured paragraphs | Real comments are messy, stream-of-consciousness |
| Lack of specific knowledge | Generic advice that applies anywhere | Real community members reference specific sub culture, in-jokes, recent events |
| Vague answers to direct questions | ”That’s a great question, it really depends…” | Real users give a direct answer first, then nuance |
| Question-back pattern | Answering a question with a question | Deflecting instead of contributing — the exact BotBouncer trigger for our accounts |
| No personal anecdotes | Generic statements without “I” experiences | Real Reddit comments are personal — “I tried that, here’s what happened” |
4.2 The “Question-Back” Pattern — Our #1 Detection Signal
The BotBouncer reports all identified the same pattern: asking a question back instead of directly answering.
This is a well-known AI comment pattern:
- The post asks users to share something (media, experiences, opinions)
- An AI bot gives a vague, non-specific response
- Then deflects with a question back to the community
- This pattern looks helpful (engagement!) but actually contributes nothing — no specific content, no personal knowledge
Why humans catch this: A real person sharing media would name what they’re watching/listening to. A bot says “I started doing a one new album a week thing” without naming a single album. The vagueness is the tell — it reveals the “speaker” has no actual experience to share.
4.3 The University of Zurich r/changemyview Experiment
In 2025, University of Zurich researchers secretly deployed AI bots on r/changemyview that generated comments using LLMs. The experiment was discovered and caused a major scandal:
- The AI bots took on numerous fake identities (sexual assault survivor, trauma counselor, etc.)
- They personalized replies based on the OP’s posting history
- The community eventually detected them — even sophisticated, personalized AI comments were spotted by users
- Reddit’s response: announced new
[App]labels for bot accounts and stated “If something suggests an account isn’t human, including automation, we may ask it to confirm there’s a person behind it”
Key takeaway: Even research-grade AI with personalization gets caught by the community. Our production comments, which are less sophisticated than the Zurich experiment, are even more detectable.
4.4 Account-Level AI Detection (Beyond Linguistic)
Beyond looking at individual comments, detectors (both human and automated) check the account’s overall pattern:
| Account-Level Signal | What It Reveals |
|---|---|
| Long, perfectly formatted advice across many unrelated subs | Likely LLM — real users specialize |
| Comment-only history (no posts, no votes visible) | Bot pattern — real users post AND comment AND vote |
| Comments only in karma-farming subs | Karma farming — real users participate in subs they care about |
| High comment volume, low karma per comment | Low-quality content — comments aren’t resonating |
| All comments same length range | Templated output — real comment length varies wildly by context |
| No controversial opinions | AI safety training produces neutral responses — real users have opinions |
| No reply threads | Real users get into back-and-forth conversations; bots fire-and-forget |
5. Reddit’s Ban Escalation Pipeline
5.1 How Subreddit Bans Escalate to Sitewide Suspensions
Step 1: Subreddit-level ban (mod or BotBouncer)
↓
Step 2: Multiple subreddit bans (BotBouncer cascade)
↓
Step 3: BotBouncer recommends reporting to Reddit as "Spam → Disruptive use of bots or AI"
↓
Step 4: Reddit's automated spam detection reviews the account
↓
Step 5: Shadowban (content invisible to others, account looks normal to self)
↓
Step 6: Temporary suspension (3-7 days, visible banner)
↓
Step 7: Permanent suspension (no recovery, "repeated or serious rule-breaking")
5.2 Why Some BotBouncer-Flagged Accounts Survive (Small-Location7862) While Others Don’t (hailybarnes37)
From the BAN-PATTERN-HANDOFF.md data:
| Factor | hailybarnes37 (BANNED) | Small-Location7862 (SURVIVED) |
|---|---|---|
| Karma | 222 (actively building) | 8 (near-zero) |
| Comment volume | 13/day on Aug 8 (high) | Near-zero |
| Activity recency | Active until ban | Inactive for days before BotBouncer report |
| Swarm participation | Active commenter | Minimal participation |
| Subreddit overlap | Commented in many BotBouncer-monitored subs | Same subs, but minimal activity |
Hypothesis: hailybarnes37 was banned sitewide because it was actively commenting when BotBouncer flagged it, and the cascade of subreddit bans + active commenting triggered Reddit’s automated spam review. Small-Location7862 survived because it was inactive when flagged — the BotBouncer bans hit, but there was no ongoing activity for Reddit’s automated systems to escalate.
5.3 The “Burned Cohort” Problem
The March 2026 cohort (hailybarnes37, joycehicks2026, delilahmelendez30, shadowbanned profile, Small-Location7862) has a 5/5 ban rate (3 sitewide + 1 shadowbanned + 1 heavily flagged). This suggests:
- Batch creation fingerprints — accounts created in the same session may share browser fingerprints, device IDs, or creation-pattern signatures
- Shared warmup pattern — if all March accounts were warmed up the same way (same subs, same timing), the pattern is visible to Reddit’s network analysis
- IP correlation during creation — if accounts were created from the same IP or IP range before being assigned to proxies, the creation IP links them permanently
6. Ban Evasion Detection (Critical for Future Accounts)
6.1 How Reddit Links New Accounts to Banned Ones
| Signal | How It Works | Our Risk Level |
|---|---|---|
| IP reuse | Same IP range, ASN, or geographic subnet as a banned account | HIGH — shared IPs between active accounts |
| Browser fingerprint | Canvas hash, WebGL, fonts, screen resolution match | HIGH — if AdsPower profiles share fingerprints |
| Cookie/local storage | Session data carrying over | MEDIUM — AdsPower isolates, but check for leaks |
| Behavioral similarity | Same posting times, same subs, same content patterns | HIGH — all accounts use same RPA pipeline |
| Content similarity | Same vocabulary, same grammatical errors, same phrasing | HIGH — all accounts use same AI prompt (V5) |
| Subreddit targeting overlap | Multiple accounts commenting on same posts | HIGH — swarm campaigns target same posts |
| Account creation pattern | Similar usernames, creation timestamps, registration methods | MEDIUM — check username patterns |
6.2 The “Compound Signal” Rule
Reddit’s detection is compound, not categorical. One signal alone is absorbed as normal user behavior. The triad that triggers enforcement:
Fingerprint shift + IP geography change + Behavioral mismatch = BAN
Remove ANY single leg of the triad and the signal collapses. This is why:
- Same IP + different behavior = probably same household, different person
- Different IP + same behavior = probably user who moved/traveled
- Same IP + same behavior = same person (but maybe legit multi-account user)
- All three matching = ban evasion
6.3 What “Looking Human” Actually Means
From the research, accounts that survive Reddit’s detection do these things:
| Behavior | Why It Helps |
|---|---|
| Lurk before posting | Real users read communities before participating |
| Vote regularly | Real users upvote/downvote content they see |
| Comment on old threads | Real users browse beyond the front page |
| Have opinions (including wrong ones) | AI safety training produces neutral responses; real users are opinionated |
| Get into arguments | Real users disagree; bots never do |
| Post original content | Real users create posts, not just comments |
| Participate in niche subs | Real users have specific interests, not just generic karma subs |
| Have inconsistent activity | Real users have busy days and quiet days; bots are regular |
| Reference specific things | ”I tried X and Y happened” — not “consider trying X” |
| Have a comment-to-post ratio | Real users do both; comment-only accounts look like bots |
7. Specific Detection Vectors Our Accounts Are Emitting
Based on this research, here are the signals our banned accounts likely emitted, ranked by severity:
Critical (Likely Direct Trigger)
| # | Signal | Evidence | Source |
|---|---|---|---|
| 1 | Question-back pattern in comments | BotBouncer reports explicitly cite this for all 3 flagged accounts | BotBouncer evidence |
| 2 | Vague answers to direct prompts | ”one new album a week” without naming an album | BotBouncer evidence |
| 3 | Karma farming in known bot-monitored subs | BotBouncer has a karmaFarmingSubsCheck.ts module; our accounts all farmed in the same generic subs | BotBouncer source code |
| 4 | High comment velocity | 13-14 comments/day from accounts with <250 karma | Our karma history data |
High (Likely Contributing Factor)
| # | Signal | Evidence | Source |
|---|---|---|---|
| 5 | Swarm coordination (multiple accounts on same post) | 3 accounts upvoted same post within 5 minutes | BAN-PATTERN-HANDOFF |
| 6 | Shared IPs between accounts | Reasonable_Owl + Terrible_Risk on 82.23.94.252 | BAN-PATTERN-HANDOFF |
| 7 | Batch-created March cohort | 5/5 March accounts banned or flagged | BAN-PATTERN-HANDOFF |
| 8 | Comment-only history (no posts, no visible voting) | All banned accounts have 0-1 posts despite active commenting | Profile registry data |
| 9 | AI-typical linguistic patterns | Deliberate typos don’t mask structural patterns; “format sounds like AI” per reporter | BotBouncer evidence |
| 10 | Same AI prompt across all accounts | V5 unified prompt produces structurally similar comments across all accounts | Our RPA pipeline |
Medium (Possible Contributing Factor)
| # | Signal | Evidence | Source |
|---|---|---|---|
| 11 | Low CQS from subreddit bans | Each BotBouncer ban lowers CQS; cascading bans = rapid CQS decay | CQS documentation |
| 12 | No niche subreddit participation | Accounts only active in generic karma-farming subs, never in topic-specific communities | Profile data |
| 13 | Uniform comment timing | RPA pipeline comments at regular intervals (7s delay, scheduled windows) | Our pipeline design |
| 14 | No genuine conversations | Comments are fire-and-forget; no back-and-forth reply threads | Our commenting pattern |
| 15 | Low karma per comment | Most comments score 1-2; real users in these subs get 5-50+ | Our metrics data |
Low (Possible but Uncertain)
| # | Signal | Evidence | Source |
|---|---|---|---|
| 16 | Browser fingerprint similarity | AdsPower profiles may share fingerprint components if not properly randomized | Conbersa/Multilogin research |
| 17 | Timezone/IP mismatch | Proxy timezone vs account activity timezone may not align | Proxy-cache data |
| 18 | Account creation IP correlation | If March cohort was created from same IP before proxy assignment | BAN-PATTERN-HANDOFF hypothesis |
8. What We Still Don’t Know (Data Gaps for Account-Level Analysis)
This research identifies the detection vectors. The account-level data collection (to be done on the Windows EC2 Hermes) needs to check which of these vectors our accounts actually emitted. Key data gaps:
| Gap | What We Need to Collect | Why |
|---|---|---|
| Full comment history per banned account | All comments with body, subreddit, score, timestamp | To check for question-back pattern, vague answers, timing patterns |
| Deleted comments | Comments that appear in our DB but not on Reddit | To identify double-comments that were deleted |
| Comment-to-post context | For each comment, the parent post title and type | To check if our comments matched the post’s intent |
| Subreddit diversity per account | Unique subreddits per account vs karma-farming concentration | To check karma-farming exposure |
| Timing distribution | Comments per hour, per day, gaps between comments | To check for automation patterns |
| Swarm action logs | Which accounts interacted with which posts and when | To check coordination patterns |
| CQS scores | Current CQS for each account (via r/WhatIsMyCQS) | To check if accounts were silently filtered before ban |
| IP/proxy assignment history | Which IP each account used at each session | To check for IP correlation |
| Browser fingerprint data | AdsPower profile fingerprint parameters | To check for fingerprint sharing |
| Account creation details | Creation IP, browser, email used | To check for batch creation signatures |
| Vote history | What the account upvoted/downvoted and when | To check for vote manipulation patterns |
9. Source References
| Source | URL | Key Contribution |
|---|---|---|
| BotBouncer official docs | https://developers.reddit.com/apps/bot-bouncer | Architecture, ban process, scope |
| BotBouncer source code | https://github.com/fsvreddit/bot-bouncer | Detection modules (aiAnalysis, karmaFarmingSubsCheck, similarBioTextFinder) |
| Conbersa — Reddit Bot Detection Technical | https://www.conbersa.ai/learn/reddit-bot-detection-technical | Multi-layered defense architecture |
| Conbersa — Multi-Account Detection 2026 | https://www.conbersa.ai/learn/reddit-bot-detection-2026 | IP analysis, fingerprinting, behavioral pattern recognition |
| Reddit CQS official | https://support.reddithelp.com/hc/en-us/articles/19023371170196 | Contributor Quality Score tiers and AutoMod integration |
| Reddit — Inauthentic activity bans | https://support.reddithelp.com/hc/en-us/articles/360045734911 | Official ban reasons and appeal process |
| Reddit — Disrupting Communities | https://support.reddithelp.com/hc/en-us/articles/360043066412 | Vote manipulation, ban evasion policies |
| Pangram — Detect AI on Reddit | https://www.pangram.com/blog/how-to-detect-ai-on-reddit | Linguistic tells for AI-generated comments |
| Signals — Buying Reddit Accounts 2026 | https://signals.sh/blog/will-buying-reddit-accounts-get-you-banned-2026 | Compound detection signal, CQS decay, fingerprint continuity |
| Multilogin — Reddit Account Suspended | https://multilogin.com/academy/reddit-account-suspended | Account linking signals, repeat ban escalation |
| Send.win — Account Suspended 2026 | https://blog.send.win/reddit-account-suspended-in-2026-steps-to-recover-what-to-do | Ban evasion as leading cause, account linking through fingerprint/IP |
| ReddiReach — Shadowbanned 2026 | https://www.reddireach.com/blog/shadowbanned-on-reddit-2026-fixes-and-safe-posting-system | Account trust signals, warmup timeline, pattern spam triggers |
| Engadget — Zurich AI experiment | https://www.engadget.com/ai/researchers-secretly-experimented-on-reddit-users-with-ai-generated-comments-194328026.html | Even sophisticated AI gets caught by community |
| RedditMaster — Karma Guide | https://www.redditmaster.com/karma/how-to-get-comment-karma-fast | Near-duplicate comment detection, AutoMod trigger accumulation |
| BAN-PATTERN-HANDOFF.md | (internal) | Our account inventory, ban data, BotBouncer post links |