YouTube Hashtag Trend Discovery — Find Trending Videos #️⃣🚀

🎉 Four hashtag operations in one tool — explore, bulk, autocomplete, expand

For social media managers, content strategists, SEO teams, and trend forecasters who need YouTube hashtag intelligence before their competitors do.

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📋 Overview

Stop guessing which YouTube hashtags actually work. This actor turns any hashtag — or any topic seed — into a structured dataset of trending videos, Shorts, suggestion lists, and related hashtag networks. Built for teams who run on data, not vibes.

Why thousands of professionals choose us:

• ✅ Four operations, one input: Explore one hashtag, bulk-audit a list, autocomplete-discover new ideas, or expand a seed into its related hashtag neighborhood — all from a single actor. No other YouTube hashtag tool offers this.
• ⚡ Adoption metadata on every row: Each video carries hashtagTotalVideos and hashtagTotalChannels (parsed straight from YouTube's hashtag panel — e.g. #music = 186M videos · 28M channels). Know which hashtags are saturated battlegrounds vs fresh territory at a glance.
• 🎯 Real trending data, not guesses: Direct from YouTube's own hashtag pages, search ranking, and autocomplete engine. Same data YouTube serves to users — clean, structured, paginated.
• 💰 Mid-tier pricing that signals quality: $0.004 per row. Below premium tools at $0.02, double the rock-bottom commodity at $0.002. Honest middle.
• 💎 Built for production: Bulk inputs up to 200 hashtags per run. Pagination up to 25 pages. Geo + language localization. Webhook-ready clean JSON output.
• ✨ NEW: Hashtag expansion mode — give us one seed hashtag, get back a structured map of related hashtags + the actual trending content for each. Perfect for SEO and content-cluster research.

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✨ Features

• #️⃣ Single Hashtag Explore: Get the latest trending videos OR Shorts for any hashtag with paginated depth control.
• 📚 Bulk Hashtag Mode: Audit up to 200 hashtags in one run — one paginated pull per hashtag, clean rows.
• 💡 Autocomplete Discovery: Seed a topic (trav, fitness, react) and get back the hashtag suggestions YouTube actually surfaces in its search box.
• 🌐 Hashtag Expansion: Pass one seed hashtag, get back BOTH the related hashtag suggestions YouTube ranks AND the trending content for them.
• 📊 Adoption Counts on Every Row: hashtagTotalVideos and hashtagTotalChannels parsed and surfaced as integers. Filter saturated hashtags out of your dataset.
• 🎥 Content Type Filter: Pick videos (long-form, full metadata) or shorts (Shorts feed) per run — no mixed rows, no surprises.
• 🌍 Geo + Language Localization: Optional country code and language code parameters — surface region-specific trending content.
• 🔄 Auto-Normalized Hashtags: We strip leading #, reject invalid whitespace tokens before charging, and emit an explicit invalid_hashtag status row when YouTube rejects an input.
• 📦 Clean JSON Output: ISO 8601 timestamps, integer view counts, canonical channel/video/short URLs — everything is webhook-ready.
• 📑 HTML Run Report: Each run writes a styled summary report (success counts, hashtag breakdown, pages fetched, duration) to the key-value store.

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🎬 Quick Start

Pass one hashtag, get back the trending videos behind it. Or pass a list of 50 hashtags and let the actor paginate each one. Three minutes from input to dataset.

curl -X POST "https://api.apify.com/v2/acts/sian.agency~youtube-hashtag-trend-discovery/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"operation":"hashtagVideos","hashtag":"music","maxPages":3,"contentType":"videos"}'

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🚀 Getting Started (3 Simple Steps)

Step 1: Pick your operation

Choose hashtagVideos (one hashtag, paginated), bulkHashtags (a list of hashtags), suggestHashtags (topic → autocomplete hashtag ideas), or hashtagExpansion (one hashtag → related hashtags + content).

Step 2: Provide your input

Drop in a hashtag without the # prefix (we auto-strip it). For bulk mode, paste a comma-separated list or one hashtag per line. For autocomplete, pass a short topic seed.

Step 3: Run and review

Hit Start. Results stream into the Apify dataset as they come in. The HTML run summary shows hashtag breakdown, success counts, and pagination depth.

That's it! In under five minutes, you'll have:

• A clean dataset of trending videos with view counts, channels, publish dates, and adoption metadata
• Hashtag suggestion lists or related-hashtag maps for content clustering
• A styled HTML report you can share with your team

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📥 Input Configuration

Field	Type	Required	Description
operation	string (enum)	Yes	One of hashtagVideos, bulkHashtags, suggestHashtags, hashtagExpansion
hashtag	string	When hashtagVideos	Single hashtag, # auto-stripped, no spaces allowed
hashtags	string	When bulkHashtags	Comma- or newline-separated list, max 200 hashtags
seedTopic	string	When suggestHashtags	Topic seed for autocomplete (e.g. trav, fitness)
seedHashtag	string	When hashtagExpansion	One hashtag to expand into related hashtags + content
contentType	string (enum)	No (default videos)	videos for long-form, shorts for Shorts feed
maxPages	integer	No (default 3)	Max paginated pages per hashtag, range 1-25
geo	string	No	2-letter country code (e.g. US, JP) for localization
lang	string	No	Language code (e.g. en, es, ja) for result language

Single hashtag example:

{
  "operation": "hashtagVideos",
  "hashtag": "music",
  "maxPages": 3,
  "contentType": "videos"
}

Bulk example:

{
  "operation": "bulkHashtags",
  "hashtags": "music\ngaming\nsolana\ntravelhacks\nhomelab",
  "maxPages": 1,
  "contentType": "videos"
}

Autocomplete discovery example:

{
  "operation": "suggestHashtags",
  "seedTopic": "trav"
}

Hashtag expansion example:

{
  "operation": "hashtagExpansion",
  "seedHashtag": "tutorial",
  "maxPages": 2
}

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📤 Output

Results are saved to the Apify dataset with 30+ fields spanning four row kinds (video, suggestion, related-hashtag, plus error/invalid statuses). Filter by rowKind or _operation to slice cleanly.

Top fields by use case

Field	Type	Description
_operation	string	Which operation produced this row (hashtagVideos, bulkHashtags, suggestHashtags, hashtagExpansion)
_sourceHashtag	string	The hashtag this row was discovered from (normalized form)
rowKind	string	video, suggestion, or related-hashtag
videoId	string	YouTube 11-char video ID
videoPageUrl	string	Canonical https://www.youtube.com/watch?v=... URL
shortsPageUrl	string	Canonical https://www.youtube.com/shorts/... URL
isShort	boolean	true for YouTube Shorts, false for long-form
videoTitle	string	Video title (long-form videos always; Shorts feed often returns null)
channelTitle	string	Channel display name
channelHandle	string	@handle for the channel
channelPageUrl	string	Canonical channel URL
viewCount	integer	Integer view count
publishDate	string	ISO 8601 publish date
hashtagText	string	The hashtag itself (e.g. #music)
hashtagTotalVideos	integer	Total videos using this hashtag (e.g. 186000000)
hashtagTotalChannels	integer	Total channels using this hashtag (e.g. 28000000)
suggestionText	string	Raw autocomplete suggestion (suggestHashtags mode)
normalizedHashtag	string	Suggestion converted to a hashtag form, ready to feed back into the actor
position	integer	1-based ordinal in the response

Example hashtag video row:

{
  "_operation": "hashtagVideos",
  "_sourceHashtag": "music",
  "rowKind": "video",
  "videoId": "BDdcoiGMRQU",
  "videoPageUrl": "https://www.youtube.com/watch?v=BDdcoiGMRQU",
  "isShort": false,
  "videoTitle": "JORJ - SNIMKITE | Official 4K Video, 2026 #music",
  "channelId": "UCZW6wX1ATmWR13R8Cj7wE4A",
  "channelTitle": "Ra Music",
  "channelPageUrl": "https://www.youtube.com/channel/UCZW6wX1ATmWR13R8Cj7wE4A",
  "viewCount": 103167,
  "viewCountText": "103,167 views",
  "lengthText": "3:00",
  "publishDate": "2026-05-07",
  "publishedAt": "2026-05-07T00:00:00Z",
  "hashtagText": "#music",
  "hashtagInfoText": "186M videos • 28M channels",
  "hashtagTotalVideos": 186000000,
  "hashtagTotalChannels": 28000000
}

Example suggestion row:

{
  "_operation": "suggestHashtags",
  "_sourceSeed": "trav",
  "rowKind": "suggestion",
  "suggestionText": "#travelhacks",
  "normalizedHashtag": "travelhacks",
  "isHashtagSuggestion": true,
  "position": 7
}

Example related-hashtag row (from hashtagExpansion):

{
  "_operation": "hashtagExpansion",
  "_sourceSeed": "tutorial",
  "rowKind": "related-hashtag",
  "hashtagText": "#tutorial",
  "hashtagInfoText": "11M videos • 2.3M channels",
  "hashtagTotalVideos": 11000000,
  "hashtagTotalChannels": 2300000,
  "position": 1
}

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💼 Use Cases & Examples

1. Social Media Manager — Hashtag Research Before Campaign Launch

Sarah runs the social team at a DTC fashion brand. She needs to pick 5 hashtags for next month's video drop — but she doesn't want to waste budget on a saturated tag with 200M competing videos.

Input: 25 candidate hashtags via bulkHashtags. Output: Every hashtag's trending videos plus adoption counts (hashtagTotalVideos, hashtagTotalChannels). Use: Sarah filters to hashtags where total videos are between 100K and 5M — the goldilocks zone — and ships the campaign on data, not gut feel.

2. Content Strategist — Discover Topical Wedges Before They Saturate

Marcus runs content strategy at a B2B SaaS shop targeting devs. He needs to find the rising YouTube hashtags around react and nextjs before everyone else piles in.

Input: suggestHashtags mode with seed topics like react, nextjs, serverless. Output: YouTube's actual autocomplete suggestions — ranked hashtag candidates straight from the search box. Use: Marcus spots #reactislands and #partialhydration as emerging hashtags weeks before they hit mainstream blog posts and prebuilds content for the wave.

3. Trend Forecaster — Weekly Hashtag Delta Tracking

Jen builds dashboards at a trend-forecasting agency. Her clients pay $5K/mo for early-signal reports — and she needs to detect rising hashtags 2-4 weeks before they hit mainstream metrics.

Input: A locked list of 50 vertical-specific hashtags, run weekly via bulkHashtags. Output: Time-series-ready rows with hashtagTotalVideos snapshots she diffs week-over-week. Use: Hashtags showing 15%+ weekly video-count growth flag as "trending up" in Jen's dashboards — the early-warning system her clients pay for.

4. SEO Team — Hashtag-to-Keyword Research at Scale

Priya leads SEO at a hospitality marketplace. She knows hashtag intent often mirrors search intent — but she needs a way to map the topical neighborhood around #travelhacks for her content cluster planning.

Input: hashtagExpansion mode with travelhacks as the seed. Output: A structured list of related hashtags YouTube surfaces (#travelessentials, #travelvlog, #solotraveltips...) each with adoption counts. Use: Priya pipes the rows into her keyword tool, cross-references search volumes, and builds 15 content briefs targeting under-served corners of the travel space.

5. AI Training Pipeline — Hashtag-Clustered Video Datasets

Diego ships training data for an open-source video understanding model. His team needs hashtag-tagged YouTube video metadata for clustering — clean, structured, with source-of-truth labels.

Input: bulkHashtags mode with the project's 80-hashtag taxonomy. Output: Thousands of video rows each carrying the _sourceHashtag they came from, ready for clustering. Use: Diego trains the model with hashtag labels as weak supervision — no manual labeling pipeline required, complete reproducibility per hashtag.

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🔗 Integration Examples

JavaScript / Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('sian.agency/youtube-hashtag-trend-discovery').call({
  operation: 'hashtagVideos',
  hashtag: 'tutorial',
  maxPages: 3,
  contentType: 'videos',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);
// → { rowKind: 'video', videoId: '...', viewCount: 103167, hashtagTotalVideos: 11000000, ... }

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

run = client.actor('sian.agency/youtube-hashtag-trend-discovery').call(
    run_input={
        'operation': 'bulkHashtags',
        'hashtags': 'music\ngaming\nsolana',
        'maxPages': 1,
    }
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item['hashtagText'], item['videoTitle'], item['viewCount'])

cURL

curl -X POST "https://api.apify.com/v2/acts/sian.agency~youtube-hashtag-trend-discovery/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"operation":"suggestHashtags","seedTopic":"react"}'

Automation Workflows (N8N / Zapier / Make)

1. Trigger: Weekly schedule, webhook, or new-row event in your CRM
2. HTTP Request: Call the actor's run endpoint with your hashtag list as JSON
3. Process: Fetch the dataset items via /datasets/{id}/items
4. Action: Write to Notion/Airtable, post to Slack, or pipe into your dashboard

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📊 Performance & Pricing

FREE Tier (Try It Now)

• Full feature access — same operations, same quality, same fields
• Perfect for testing your hashtag list before scaling
• No credit card required

PAID Tier (Production Ready)

• Unlimited hashtags per run (up to the 200-per-run input cap)
• Pay-per-row: only billed for successful row pushes (invalid hashtags and errors are NEVER charged)
• Auto-laddered pricing — BRONZE through DIAMOND tier discounts apply automatically as you scale

💰 Mid-tier honest pricing — $0.004 per hashtag-video row puts us below the premium tools ($0.02+) and above the race-to-the-bottom commodity ($0.002 — fragile data, no metadata). The middle is where reliable bulk research lives.

🔗 View current pricing

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❓ Frequently Asked Questions

Q: How many hashtags can I process per run? A: Up to 200 hashtags in bulkHashtags mode. Single-hashtag operations have no limit on output rows — only on pagination depth (1-25 pages). For larger workloads, split into multiple runs.

Q: Does the # prefix matter on input? A: No. We auto-strip leading # so music, #music, and #music are all treated identically. Hashtags containing whitespace are rejected before charging with a clear error message — YouTube doesn't accept them.

Q: Why do Shorts rows have fewer fields than videos rows? A: YouTube's Shorts feed itself returns sparser metadata than its videos feed — title, channel, and publish date are not in the Shorts response payload. We surface every field YouTube actually provides; we don't fabricate the rest. The viewCount and videoId are always present.

Q: What happens if I pass an invalid hashtag? A: One row is pushed with status: 'invalid_hashtag' and a clear errorMessage, and you are NOT charged for it. You'll never lose budget to typos or YouTube-rejected inputs.

Q: Can I get both videos AND Shorts in one run? A: Not in one operation — contentType is videos OR shorts, not both. The reason is that YouTube returns them via separate endpoints with different rate-limit budgets, and mixing them would make per-row pricing unfair. To get both, run twice.

Q: How accurate is hashtagTotalVideos? A: We parse YouTube's own hashtag-info panel string (e.g. "186M videos • 28M channels") into integers. The number itself is YouTube's published count and updates on their refresh cadence. Use it for relative ranking — it's the same number that powers their UI.

Q: Is the data live or cached? A: Live — every run pulls fresh data from YouTube's hashtag, search, and autocomplete endpoints. No caching layer between you and YouTube.

Q: Is this legal? A: Yes — we only access publicly visible YouTube data (the same data YouTube serves to any logged-out browser visiting a hashtag page). See our legal section below.

YouTube® is a trademark of Google LLC. This actor is an independent discovery tool. It is not affiliated with, endorsed by, or sponsored by Google LLC or YouTube.

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🐛 Troubleshooting

"Hashtag contains whitespace" error before run starts

• YouTube hashtags are single tokens — machine learning is invalid. Use the joined form machinelearning instead. We reject this BEFORE charging so you don't lose budget on typos.

Only 1 page fetched even though maxPages is higher

• YouTube's pagination is best-effort — the continuation token isn't always returned (depends on hashtag size, request rate, time of day). The actor stops when YouTube stops handing out tokens. The first page typically contains 25-36 rows of high-quality content already.

Shorts rows are missing title / channelTitle

• This is upstream behavior, not a bug. YouTube's Shorts feed endpoint returns sparser metadata than the videos endpoint — they only return videoId, viewCount, and thumbnail data. Use hashtagVideos with contentType: videos if you need full metadata.

Empty dataset with status: 'invalid_hashtag' row

• YouTube returned "This hashtag is invalid" or "Not much to see right now" for the input. Hashtag may not exist, may contain unsupported characters (emoji, certain non-Latin scripts), or has zero indexed content. Try a different spelling or a parent hashtag.

Bulk mode returning very different row counts per hashtag

• Normal — niche hashtags (#solana ≈ 21 videos per page) return fewer rows than mainstream ones (#shorts ≈ 36). This is the actual content YouTube has for each hashtag.

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⚖ Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

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🤝 Support

Join our active support community

• For issues or questions, open an issue in the actor's repository
• Check SIÁN Agency Store for more automation tools
• 📧 apify@sian-agency.online

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