Free Twitter Bot Checker — Live Account Score (BotBlock) | ReplySocial
X (Twitter) Bot Checker
Paste any X (Twitter) handle for an instant bot likelihood score. Powered by BotBlock — the same engine ReplySocial uses to filter spam replies in real time — this free checker analyzes 30+ signals across account metadata, posting behavior, and language patterns to classify any account as Human, Suspicious, or Spam.
Bot detection on X in 2026 is harder than it was five years ago. Modern spam accounts use AI-generated text, mature accounts (purchased and aged), and human-shaped posting patterns to defeat naive checks. A robust detector has to combine signals across several categories.
BotBlock uses 30+ signals across three layers. Metadata signals look at account age, follower-to-following ratio, blue check (paid vs verified), bio content, and known scam patterns like Telegram or WhatsApp links. Behavioral signals track reply velocity (sub-30-second median gaps are bot-class), 24/7 activity, and content duplication across posts. Linguistic signals catch AI-vocabulary clustering, sycophantic openers, hidden Unicode, em-dash abuse, and tier-keyed scam phrases. No single signal triggers a verdict; tiers come from converging evidence.
Botometer is the academic incumbent — built at Indiana University and widely cited in research. It's a strong tool for batch analysis and academic work, but it's slow, dated on modern AI-bot patterns, and not built for real-time inbox filtering. BotBlock is built for the latter use case: low-latency, modern signals, integrated with a reply inbox.
X's native spam filter operates platform-wide and is intentionally conservative — it only catches the most obvious cases to avoid false positives at scale. The gray zone (rapid-reply farms, AI-generated answers, scam-link bios) consistently passes X's filter but gets caught by BotBlock. The two are complementary; a serious power user benefits from both.
If you're trying to spot a bot manually without our tool, focus on five tells. (1) Account age combined with high posting volume — a 90-day-old account with 5,000 posts is almost certainly automated. (2) Follower-to-following ratio above 3:1 in the wrong direction (following >> followers). (3) Bio content with Telegram/WhatsApp links or AI buzzword clusters. (4) Reply timestamps clustering at sub-minute gaps. (5) Em-dash overuse (more than 2% of characters) combined with AI-vocabulary words like "delve", "tapestry", "multifaceted", or "holistic."
BotBlock looks at all five plus 25 more, weighs them dynamically (positive signals like established account age can offset minor red flags), and renders a single 0-10 score with the contributing signals visible.
The instinct is to only check accounts that look suspicious. The problem is that modern spam accounts are designed to look normal at a glance. They use real photos, plausible bios, and scattered original posts. The signals that give them away are quantitative, not qualitative — the kinds of patterns a human can't spot in two seconds.
ReplySocial scores every X reply automatically in the background, so by the time you see a reply in your inbox you already know the tier. The Hide-bots filter then removes them in one click. This free tool runs the same engine on demand for any account, so you can spot-check before engaging publicly with someone whose vibe seems off.
Beyond this free tool, ReplySocial monitors X, Reddit, LinkedIn, and Facebook from one inbox. See how the unified inbox works, or compare us to other tools — like our Hootsuite alternative breakdown.
BotBlock combines 30+ signals across metadata, behavior, and language to score every account on a 0-10 scale. The algorithm requires multiple converging signals before flagging Suspicious (3.0+) or Spam (8.0+), so false positives stay low. No detector is perfect on modern AI-bot patterns, but the combined signal-set catches most accounts X's native filter misses.
Metadata: account age, follower ratio, blue check (paid vs verified), bio content, Telegram/WhatsApp links, sentence-length display names, AI buzzwords. Behavioral: reply velocity (sub-30s median gaps), 24/7 activity, content duplication. Linguistic: AI vocabulary clustering, em-dash abuse, sycophantic openers, hidden Unicode characters, tier-keyed scam phrases. Each signal has a weight; converging signals push the score up.
Rarely. The Suspicious tier (3.0-7.9) requires multiple converging red flags, and positive signals like established account age and healthy follower ratio actively reduce the score. New accounts are not flagged on age alone. The most common case (real humans posting normally) renders no badge — the inbox stays clean.
Botometer is the academic incumbent (Indiana University) — strong for research, but slow and dated on modern AI-bot patterns. BotBlock is built for real-time inbox filtering: low-latency, modern signals (AI vocabulary, scam phrases, hidden Unicode), and integrated with a reply triage workflow. Use Botometer for batch academic analysis, BotBlock for live engagement decisions.
Modern spam accounts are designed to look normal at a glance — real photos, plausible bios, scattered original posts. The signals that give them away are quantitative, not qualitative; a human can't spot them in two seconds. BotBlock scores every reply author in the background so by the time you see the reply, the tier is already there. It's the only practical way to keep the inbox clean as volume scales.
Yes. ReplySocial Pro auto-scores every X reply author in your inbox, surfaces tier badges (Human, Suspicious, Spam), and gives you a one-click Hide-bots filter. The free public tool above runs the same engine on demand for any account; the paid product runs it continuously across every reply your inbox receives.
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Calculate engagement rates from likes, retweets, replies, and bookmarks — with benchmarks by follower tier.
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