Pricing

$19.99/month + usage

TikTok Trending Hashtags With Momentum Analytics Scraper

TikTok Trending Hashtags Scraper detects and tracks trending TikTok hashtags, returning metrics like popularity score, usage volume, top videos, top creators, geographic trends, avg views/engagement, and trend velocity.

Pricing

$19.99/month + usage

Rating

0.0

(0)

Developer

SimpleAPI

Actor stats

Bookmarked

Total users

Monthly active users

3 days ago

Last modified

Field	Example Value	Use Case
`hashtag_id` / `hashtag_name`	`"1234567890"` / `"fyp"`	Identify the hashtag
`country_info` / `industry_info`	`{"label": "United States"}` / `{"label": "Tech & Electronics"}`	Confirm scope labels applied to this row
`rank` / `rank_diff_type` / `rank_change_readable`	`3` / `"up"` / `"↑ 2 spots"`	See current rank and how it changed
`trend_direction`	`"rising"`	The core curve-shape signal for this hashtag
`video_views` / `publish_cnt`	`4200000000` / `182000`	Reach and content-volume signals
`views_velocity` / `rank_momentum`	`12500000.0` / `-0.5`	Real computed growth rate per day (see below)
`days_in_top100`	`14`	How long this hashtag has sustained Top 100 status
`period`	`"7"`	Confirm the trend window applied
`trend`	raw analytics blob	Full trend-curve data (Deep Analytics mode)
`hashtag_url`	`"https://www.tiktok.com/tag/fyp"`	Direct link to the hashtag page
`first_seen_at` / `scraped_at`	first-tracked date / collection timestamp	Confirm tracking history and freshness

Real cross-run momentum — schedule repeat runs to populate it

views_velocity, rank_momentum, days_in_top100, and the history-based rank_diff_type/rank_change_readable come from a genuine persistent history store, keyed by hashtag + country + period, compared across runs. The first run for any given hashtag+country+period combination always reports null velocity — there's no prior data point to compare against yet. Schedule this Actor to run repeatedly against the same country/period settings to build real velocity data over time. Turning enableMomentumTracking off skips history read/write entirely, and every momentum field will be null.

Honest limitations disclosed in the schema itself

Two things worth knowing before you rely on this data: industryFocus is echoed onto output rows (industry_info) but does not actually narrow TikTok's anonymous preview results server-side — this was verified live against the platform and is disclosed directly in the input schema, not hidden. Similarly, maxHashtags is an upper bound, not a guarantee — TikTok's anonymous preview may return fewer hashtags than requested, and the Actor reports the real count rather than padding it to match your requested limit.

Why not build this yourself?

TikTok has no public, documented API for trending-hashtag discovery with momentum tracking — the anonymous preview surface this Actor uses isn't intended for third-party bulk access, and building momentum tracking on top of it means maintaining a persistent, correctly-keyed history store across runs yourself. All of that — including the honest first-run-null behavior and the industry-filter limitation disclosure — is already implemented in this Actor's source.

How to use data extracted from TikTok?

Content strategy and trend-jacking

Track trend_direction/views_velocity for your niche's hashtags on a scheduled weekly run to catch hashtags accelerating early, before they peak — planning content around genuinely rising tags rather than ones already past their growth phase.

Agencies managing multiple client niches

Run one targetCountry/industryFocus combination per client niche on a schedule and compare rank_momentum across niches to prioritize which trend to brief content creators on first.

Market and platform research

Compare days_in_top100 distributions across countries to understand how quickly trends cycle through TikTok's Top 100 in different markets.

AI agents and automated pipelines

An agent can call this Actor on a schedule to feed a trend-monitoring dashboard, filtering on trend_direction: "rising" and a minimum views_velocity to surface only genuinely accelerating hashtags for further action.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
`dataMode`	No	string (enum)	`trending` (default), `trending_deep`, or `watchlist`	`"trending"`
`targetCountry`	No	string (enum)	Country to pull trending hashtags for	`"US"` (default)
`trendWindowDays`	No	string (enum)	`7` (default), `30`, or `120`	`"7"`
`curveWindowDays`	No	string (enum)	Watchlist-mode curve window: `7`, `30` (default), or `120`	`"30"`
`industryFocus`	No	string (enum)	Industry label echoed on output; does not filter anonymous results	`""` (all industries, default)
`onlyNewEntrants`	No	boolean	Echo intent for newly-trending hashtags	—
`watchlistHashtags`	No	array	Hashtags (no `#`) to track in Custom Watchlist mode	`["fyp"]`
`maxHashtags`	No	integer (1-500)	Upper bound on hashtags fetched; real count may be lower	`100` (default)
`enableMomentumTracking`	No	boolean	Read/write persistent cross-run history for velocity fields	`true` (default)
`proxyConfiguration`	No	object	Optional proxy; falls back to direct connection	`{"useApifyProxy": false}`

{
  "dataMode": "trending",
  "targetCountry": "US",
  "trendWindowDays": "7",
  "maxHashtags": 100,
  "enableMomentumTracking": true
}

Common pitfall: the first run against any new targetCountry/trendWindowDays combination will always have null velocity fields — this isn't a bug, it's the honest absence of a prior comparison point. Schedule repeat runs to build history.

🔽 Output sample

Output is typed, normalized JSON — one row per trending or watchlisted hashtag (exportable as JSON, CSV, or Excel).

{
  "hashtag_id": "1234567890",
  "hashtag_name": "fyp",
  "country_info": {"label": "United States", "value": "US"},
  "industry_info": {"label": "All Industries", "value": ""},
  "is_promoted": false,
  "rank": 3,
  "rank_diff_type": "up",
  "rank_change_readable": "↑ 2 spots",
  "trend_direction": "rising",
  "video_views": 4200000000,
  "views_velocity": 12500000.0,
  "rank_momentum": -0.5,
  "days_in_top100": 14,
  "publish_cnt": 182000,
  "period": "7",
  "hashtag_url": "https://www.tiktok.com/tag/fyp",
  "first_seen_at": "2026-07-12T00:00:00Z",
  "scraped_at": "2026-07-26T14:02:11Z"
}

All 19 fields shown above are the Actor's real dataset columns — this is the full output shape, not a subset.

How do you filter and target specific hashtags?

dataMode is the category axis — the full trending list, the trending list plus deep analytics, or a custom watchlist of exact hashtags you name. targetCountry and industryFocus scope the trending list (though note the industry filter's honestly-disclosed limitation on anonymous requests). onlyNewEntrants is the closest thing to a quality signal, flagging newly-trending hashtags. Volume is controlled by maxHashtags (an upper bound, not a guarantee), and trendWindowDays/curveWindowDays control the time window the trend curve is computed over.

{ "dataMode": "trending", "targetCountry": "GB", "trendWindowDays": "30", "onlyNewEntrants": true }

{ "dataMode": "watchlist", "watchlistHashtags": ["fyp", "viral", "trending"], "curveWindowDays": "30" }

{ "dataMode": "trending_deep", "targetCountry": "US", "industryFocus": "9000000002", "maxHashtags": 50 }

▶️ Want to try other trend/momentum scrapers?

Scraper Name	What it extracts
TikTok Trending Videos Insights With Hashtag Trend Ad Matcher	Trending TikTok video/ad insights matched to hashtag trends
TikTok Trending Videos Scraper	Trending TikTok videos, views, and publish dates
Google Trends Scraper: Rising & Breakout Keywords	Comparable keyword-spike detection for Google Trends
Instagram Related Hashtag Stats Scraper By Keyword Search	Comparable hashtag research for Instagram
Instagram Hashtag Scraper: Content Planner	Hashtag content and best-posting-time planning
Pinterest Search & Pin Engagement Scraper	Comparable engagement-ranked trend data for Pinterest

How to extract TikTok data programmatically

Every run is a standard Apify Actor call — one API request with your token, JSON input, JSON results back.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "dataMode": "trending",
    "targetCountry": "US",
    "trendWindowDays": "7",
    "enableMomentumTracking": True,
}

run = client.actor("<YOUR_USERNAME>/tiktok-trending-hashtags-with-momentum-analytics-scraper").call(run_input=run_input)

for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["hashtag_name"], row["rank"], row["trend_direction"], row["views_velocity"])

Export to spreadsheets or CRM

Export the dataset directly as CSV or Excel, then map hashtag_name/rank to a trend-tracking spreadsheet and views_velocity/trend_direction to a prioritization column for content planning.

Trending hashtag names, view counts, and rank data are aggregate content statistics, not personal data tied to individual users, so this is closer to public content/business data than personal data. TikTok's trending hashtag list is a publicly visible feature of the platform. Consult legal counsel for commercial applications involving bulk automated querying of TikTok's services.

❓ FAQ

Why is views_velocity null on my first run?

Because there's no prior data point to compare against yet — velocity is computed from the difference between this run and the previous run for the same hashtag+country+period. Schedule repeat runs to start populating it.

Does industryFocus actually filter results?

Not on TikTok's anonymous preview surface — this was verified live and is disclosed directly in the schema. The value is still echoed onto output rows as a label, but it doesn't narrow the returned hashtag set server-side.

Will I always get exactly maxHashtags results?

No — maxHashtags is an upper bound. TikTok's anonymous preview may return fewer hashtags than requested, and the Actor reports the real count rather than padding the result set.

It's matched against today's live trending preview; a hashtag not currently present in that preview is reported as unresolved, never fabricated with guessed data.

It has no dedicated MCP server registration in this Actor's current setup, but it is callable as a standard Apify API endpoint by any agent framework that can make an authenticated HTTP request and parse JSON.

How does this compare to other TikTok hashtag scrapers?

No independently verifiable competitor listing could be confirmed at the time of writing, so no specific comparison is made here. This Actor's distinguishing feature is genuine cross-run momentum tracking via a persistent history store, with honest null-on-first-run behavior rather than a fabricated trend.

Yes — set dataMode to watchlist and list your target hashtags in watchlistHashtags.

Conclusion

TikTok Trending Hashtags With Momentum Analytics Scraper turns a snapshot trending list into a real growth-tracking tool — genuine cross-run velocity and rank momentum, honestly null until you've scheduled enough runs to build history. It's built for marketers, creators, and agencies who need to catch hashtags accelerating, not just trending. Schedule this Actor to run repeatedly against your target country to start building momentum data.

Tiktok Trending Hashtags Analytics Scraper With Trend Creators

simpleapi/tiktok-trending-hashtags-analytics-scraper

TikTok Trending Hashtags Analytics Scraper tracks rising TikTok hashtags and extracts metrics like popularity, usage volume, top videos, creators, sounds, and engagement trends. Perfect for discovering viral topics, planning content, analyzing trends, and automating TikTok hashtag insights