How ranking actually works on TikTok, Instagram, YouTube, LinkedIn, X, and Facebook — the top signals, how new content gets distributed, and what suppresses reach. Official platform statements are separated from third-party analysis, with every claim sourced.
Last verified · 2026-05-29 · by Moe Ameen
The single ranking signal that matters most, per platform: TikTok — watch-time / completion rate; Instagram — watch time, likes-per-reach, and sends (DM shares); YouTube — viewer satisfaction (surveys + retention), not just watch time; LinkedIn — dwell time and substantive comments; X — replies and retweets (a retweet outweighs a like ~2×); Facebook — how likely the post is to spark a conversation among friends. Across every platform, original content beats reposted/watermarked content, and negative signals (hides, blocks, "not interested") suppress reach faster than positive signals lift it.
Most "algorithm explained" posts state third-party guesses as fact. This index tags every claim. TikTok, Instagram, YouTube, and Facebook publish official (if high-level) explanations of their ranking systems — those statements are marked OFFICIAL below. X open-sourced its ranking code in 2023, so its weights are real but dated (X is mid-migration to a Grok-based ranker). LinkedIn officially documents only dwell time; the rest of its model is third-party analysis. Where a mechanic is commonly repeated but not officially confirmed (TikTok’s test-cohort loop, the Instagram repost cap, the "80 billion signals" figure), it is flagged as third-party rather than presented as fact.
| Platform | Top signal | What suppresses reach |
|---|---|---|
| TikTok (For You feed) | In TikTok’s stated order: (1) user interactions — likes, shares, comments, and especially watch time / whether you finish a video; (2) content information — sounds, hashtags, captions; (3) device and account settings (weakest). | Content ineligible for the For You feed (e. |
| Instagram (Reels + Feed) | Instagram ranks each surface separately, but Adam Mosseri named three cross-surface signals (Jan 2025): watch time, likes-per-reach, and sends-per-reach (DM shares). | Instagram officially makes reels "less visible" when they are watermarked (e. |
| YouTube (long-form + Shorts) | Two official categories: Viewer Personalization (your watch history, search history, subscriptions, likes) and Content Performance (whether viewers click, watch, and positively engage). | "Not interested" / "don’t recommend this channel" feedback, dislikes, low satisfaction scores, misleading metadata/clickbait, and reused content all reduce recommendation. |
| LinkedIn officially documents one feed signal in depth: dwell time (how long a post holds attention). | Engagement bait ("Comment YES if you agree!"), and — per widespread third-party reporting, not official confirmation — outbound external links in the post body and low-dwell content. | |
| X (Twitter, For You) | From X’s open-sourced 2023 ranking code: replies and retweets carry the highest positive weight, then profile clicks / bookmarks / link clicks, then likes (lowest). | Blocks, reports, and mutes downrank hardest (several times the weight of a like). |
| Facebook (Feed) | Meta lists the inputs as: who created the post and how you’ve interacted with them before, the post format (photo/video/link), and how many of your friends engaged. | Engagement bait, clickbait, and links to low-quality/ad-farm sites are downranked (Meta integrity disclosures + third-party). |
Top ranking signals: In TikTok’s stated order: (1) user interactions — likes, shares, comments, and especially watch time / whether you finish a video; (2) content information — sounds, hashtags, captions; (3) device and account settings (weakest). TikTok says interaction signals "are generally weighted more heavily than others," and completion of a longer video carries more weight than a weak signal like shared location.
How new content gets distributed: The widely-repeated "new videos get tested on a small audience, then expand if completion is strong" loop is third-party characterization (Buffer, Hootsuite) — TikTok’s official support page does not describe a fixed test-cohort mechanic. Treat the specifics as inference, the direction as sound.
What suppresses reach: Content ineligible for the For You feed (e.g. extreme fitness/dieting), Community Guidelines violations, and "not interested" feedback. Third-party sources add watermarked/reposted and duplicate uploads.
The actionable takeaway: Optimize for completion rate above all. Hook in the first second and keep the video tight enough that viewers finish it — TikTok explicitly weights a full watch over almost everything else.
Sourcing: OFFICIAL: TikTok Newsroom + Support ("How TikTok recommends content"). The test-cohort loop is third-party (Buffer, Hootsuite).
Top ranking signals: Instagram ranks each surface separately, but Adam Mosseri named three cross-surface signals (Jan 2025): watch time, likes-per-reach, and sends-per-reach (DM shares). Likes-per-reach weighs more for reaching existing followers; sends-per-reach weighs more for reaching non-followers. Feed and Reels each layer your activity + your history with the poster on top.
How new content gets distributed: Instagram splits distribution into Connected Reach (followers) and Unconnected Reach (recommendations to non-followers). Reels expand into recommendation surfaces based on early watch time and sends. The "small test then expand" framing is third-party (Later, Buffer); the connected/unconnected split is official.
What suppresses reach: Instagram officially makes reels "less visible" when they are watermarked (e.g. a TikTok logo) or have already been posted on Instagram (recycled/aggregated). Original, unwatermarked content is favored. The specific "10+ reposts in 30 days = excluded" number is a third-party claim, not official.
The actionable takeaway: Post original, unwatermarked content; optimize the first ~3 seconds for watch-through; and engineer DM shares (sends) — that is the signal that unlocks reach to people who don’t follow you yet.
Sourcing: OFFICIAL: "Instagram Ranking Explained" + Mosseri (Jan 2025). The repost-cap number is third-party (Dataslayer).
Top ranking signals: Two official categories: Viewer Personalization (your watch history, search history, subscriptions, likes) and Content Performance (whether viewers click, watch, and positively engage). Crucially, YouTube officially weights satisfaction-survey feedback — not just raw watch time — so a video people rate highly can outperform one with more passive minutes.
How new content gets distributed: The homepage leans on watch history; "watch next" uses the current video as the primary signal. For Shorts, third-party analysis (Hootsuite, Shortimize) reports swipe-through rate, loop/replay rate, and shares replace click-through-rate as the core signals — you swipe, not click. The Shorts-specific mechanics are not in YouTube’s official Help text.
What suppresses reach: "Not interested" / "don’t recommend this channel" feedback, dislikes, low satisfaction scores, misleading metadata/clickbait, and reused content all reduce recommendation.
The actionable takeaway: Build genuine satisfaction (retention + positive feedback), not just clicks — YouTube uses survey feedback as a direct input. For Shorts, maximize loop/replay and swipe-through. Publish 2–3 hours before peak so the system can index and test before traffic arrives.
Sourcing: OFFICIAL: YouTube Help ("How YouTube recommendations work"). Shorts swipe/loop specifics and the "80 billion signals/day" figure are third-party (Hootsuite/Shortimize), not on official pages.
Top ranking signals: LinkedIn officially documents one feed signal in depth: dwell time (how long a post holds attention). Third-party analysis adds a three-stage flow — quality/spam filter → small-sample engagement test → network-relevance ranking — with thoughtful comments and relationships ranking high and likes ranking weak.
How new content gets distributed: A post is shown to a small slice of your network; strong first-hour engagement (especially comments and long dwell) expands it to 2nd- and 3rd-degree connections. This staged model is third-party (Hootsuite, AuthoredUp), not officially documented beyond the dwell-time post.
What suppresses reach: Engagement bait ("Comment YES if you agree!"), and — per widespread third-party reporting, not official confirmation — outbound external links in the post body and low-dwell content.
The actionable takeaway: Write posts that earn long dwell time and substantive comments. AuthoredUp’s dataset found posts with 61+ seconds of dwell hit ~15.6% engagement vs ~1.2% for 0–3 second dwell — the strongest reach lever on the platform.
Sourcing: OFFICIAL: only "Understanding feed dwell time" (LinkedIn Engineering). The 3-stage flow + 15.6% dwell stat are third-party (AuthoredUp/Hootsuite).
Top ranking signals: From X’s open-sourced 2023 ranking code: replies and retweets carry the highest positive weight, then profile clicks / bookmarks / link clicks, then likes (lowest). One widely-cited formula from the code weights a retweet at 1.0 and a like at only 0.5. Negative actions dwarf positives — a block scores roughly −3.0, a report ≈ −1.5.
How new content gets distributed: Roughly 500M daily posts are narrowed to ~1,500 candidates per user, drawn from in-network (people you follow) and out-of-network (interest-matched) pools. The exact 50/50 in/out split is third-party; the GitHub README states the search index supplies "~50% of posts."
What suppresses reach: Blocks, reports, and mutes downrank hardest (several times the weight of a like). Visibility filters can hard-filter or downrank. Third-party sources report external-link posts are deprioritized and X Premium gives a discovery boost (not officially quantified).
The actionable takeaway: Optimize for replies and retweets, not likes — and never post anything that triggers blocks/reports/mutes, since those negative weights overwhelm any positive engagement.
Sourcing: SOURCE: X open-source repo (twitter/the-algorithm) + code analysis — the official engineering blog now 403s, and X is migrating to a Grok-based ranker, so these legacy weights may be stale.
Top ranking signals: Meta lists the inputs as: who created the post and how you’ve interacted with them before, the post format (photo/video/link), and how many of your friends engaged. The system predicts how likely you are to comment, how likely your friends are to comment, and whether the post will spark a conversation.
How new content gets distributed: A four-step official process: gather all eligible posts → a lightweight model selects ~500 of the most relevant → heavier models score relevance/value → final ranking balances content types so formats don’t repeat. Meta stresses the models "are dynamic and change frequently."
What suppresses reach: Engagement bait, clickbait, and links to low-quality/ad-farm sites are downranked (Meta integrity disclosures + third-party). Outbound-link posts historically reach less than native content (third-party).
The actionable takeaway: Post content likely to spark genuine comments and conversation among friends — Meta explicitly predicts "how likely the post is to spark a conversation" as a ranking input.
Sourcing: OFFICIAL: Meta Transparency Center (Facebook Feed ranking). Link-reach penalties are third-party.
Primary sources are each platform’s own creator/engineering documentation, with corroborating analysis from Hootsuite, Buffer, Sprout Social, and AuthoredUp where the official record is thin:
Algorithms change constantly and platforms disclose only partial detail; this index is versioned for 2026 and re-audited quarterly. Where a claim is third-party, it is labeled so you can weight it accordingly.
Strip away the per-platform specifics and the same three rules hold everywhere: (1) the algorithm optimizes for completed, satisfying consumption, not raw exposure — finish rate, dwell time, and satisfaction beat impressions; (2) original, native, unwatermarked content is favored over reposts and cross-platform leftovers; (3) negative signals (hides, blocks, "not interested," low satisfaction) cut reach faster than positive engagement builds it. Build for the first rule, avoid the third, and post natively to each platform — which is exactly what repurposing per-platform (not cross-posting one asset) is for.
TikTok weights watch-time / completion rate; Instagram weights watch time, likes-per-reach, and DM sends; YouTube weights viewer satisfaction (surveys plus retention), not just watch time; LinkedIn weights dwell time and substantive comments; X weights replies and retweets over likes; Facebook weights how likely a post is to spark a conversation among friends.
TikTok, Instagram, YouTube, and Facebook publish official high-level explanations of their ranking systems. X open-sourced its 2023 ranking code, so its weights are real but dated. LinkedIn officially documents only dwell time; the rest of its model is third-party analysis. Every claim in this index is tagged official or third-party.
Yes. Instagram officially makes reels less visible when they are watermarked or already posted elsewhere, and original, native, unwatermarked content is favored on every platform. Posting natively to each destination beats cross-posting one asset.
On most platforms, yes. Hides, blocks, mutes, reports, and "not interested" feedback suppress reach faster than likes and shares lift it. In X's open-source code a block scores roughly -3.0 against a like at 0.5.
Constantly. Platforms tune their ranking systems frequently and disclose only partial detail. This index is versioned for 2026 and re-audited quarterly; treat third-party-labeled claims as the most likely to drift.
Algorithms optimize for completed, satisfying consumption (finish rate, dwell time, satisfaction) over raw exposure; they favor original native content over reposts; and they cut reach via negative signals faster than positive engagement builds it.
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