TikTok Trend & Discovery Scraper

Discover what's trending on TikTok — scrape hashtags, topics, and viral videos into structured datasets for research, content planning, and AI analysis.

What does this actor do?

TikTok Trend & Discovery Scraper is an Apify Actor that helps you discover trending content on TikTok by scraping search results and hashtag pages. It collects structured, analytics-ready data you can use for trend monitoring, content ideation, competitive research, and AI-powered workflows.

It extracts two types of records:

• Video records — video metadata, engagement metrics (plays, likes, comments, shares, saves), author info, hashtags, and computed scores
• Hashtag records — hashtag entity data with view counts, video counts, and descriptions

Every record is tagged with the input query that produced it, making it easy to compare results across keywords and hashtags.

Why use this actor?

• Trend intelligence, not just scraping — discover what's trending across keywords, hashtags, and topics in a single run
• Normalized output — consistent schema across all discovery modes, ready for CSV, JSON, or API consumption
• Engagement scoring — computed fields like engagementTotal, engagementRateProxy, and discoveryScore help you identify viral content instantly
• Multi-source discovery — combine keyword searches and hashtag pages in one run
• AI-ready datasets — feed results into clustering, summarization, trend scoring, and reporting pipelines
• Part of a suite — pairs with TikTok Video Scraper and TikTok Comment Scraper for a full discovery-to-analysis workflow

Input

Parameter	Type	Default	Description
searchQueries	string[]	["trending"]	Keywords or topics to discover trending content for
hashtags	string[]	["tesla"]	Hashtags to inspect (with or without #)
startUrls	object[]	[]	Optional TikTok search or hashtag URLs to scrape directly
resultsPerQuery	integer	50	Maximum number of results per keyword or hashtag (1–500)
includeVideos	boolean	true	Include discovered video records in output
includeHashtags	boolean	true	Include hashtag entity records in output
sortBy	string	"default"	Sort order for search results: default, relevance, latest, popular
debugMode	boolean	false	Enable verbose logging
proxyConfiguration	object	—	Apify proxy configuration (residential recommended)

Sample input: Keyword discovery

Discover trending content around specific topics.

{
  "searchQueries": ["ai tools", "productivity hacks", "skincare routine"],
  "resultsPerQuery": 30,
  "includeVideos": true,
  "includeHashtags": true,
  "sortBy": "popular",
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Sample input: Hashtag + keyword mixed discovery

Combine hashtag and keyword discovery in a single run to compare trends across topics.

{
  "searchQueries": ["electric vehicles", "sustainable energy"],
  "hashtags": ["tesla", "rivian", "evlife"],
  "resultsPerQuery": 50,
  "includeVideos": true,
  "includeHashtags": true,
  "sortBy": "default",
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Output

The actor outputs a normalized dataset where every record has a recordType field (video or hashtag). Results are available in four dataset views:

• Discovery Overview — combined view of all record types with key metrics
• Videos — full video details with engagement and author data
• Hashtags — hashtag entity records with view and video counts
• Engagement — video engagement metrics and computed scores

Video record example

{
  "recordType": "video",
  "inputType": "searchQuery",
  "inputValue": "trending",
  "position": 1,
  "collectedAt": "2026-03-15T04:07:31.271Z",
  "sourceUrl": "https://www.tiktok.com/search/video?q=trending",
  "sourceType": "search",
  "videoId": "7572437550057540894",
  "videoUrl": "https://www.tiktok.com/@rch._.dance/video/7572437550057540894",
  "description": "Fun trio dance trend 2025 #fypシ゚viral #trending #trio #dancetrend #funny",
  "createTime": "2025-11-14T04:45:37.000Z",
  "duration": 15,
  "region": null,
  "language": "en",
  "musicId": "7428648255015455520",
  "musicTitle": "original sound",
  "musicAuthorName": "Maddie Borge",
  "authorId": "7409315146052912174",
  "authorUniqueId": "rch._.dance",
  "authorNickname": "RCH_dance",
  "authorProfileUrl": "https://www.tiktok.com/@rch._.dance",
  "authorAvatarUrl": "https://p16-common-sign.tiktokcdn-us.com/...",
  "authorVerified": false,
  "authorFollowerCount": 14400,
  "authorHeartCount": 1200000,
  "authorVideoCount": 7,
  "likeCount": 1100000,
  "commentCount": 4388,
  "shareCount": 187200,
  "playCount": 27600000,
  "saveCount": 271300,
  "repostCount": 0,
  "hashtags": ["fypシ゚viral", "trending", "trio", "dancetrend", "funny"],
  "mentions": [],
  "thumbnailUrl": "https://p19-common-sign.tiktokcdn-us.com/...",
  "coverUrl": "https://p16-common-sign.tiktokcdn-us.com/...",
  "isAd": false,
  "isPinned": false,
  "isSponsored": false,
  "searchKeyword": "trending",
  "matchedHashtag": null,
  "engagementTotal": 1562888,
  "engagementRateProxy": 0.056626,
  "discoveryScore": 8.01
}

Hashtag record example

{
  "recordType": "hashtag",
  "inputType": "hashtag",
  "inputValue": "tesla",
  "position": 1,
  "collectedAt": "2026-03-15T04:07:30.443Z",
  "sourceUrl": "https://www.tiktok.com/tag/tesla",
  "sourceType": "hashtag",
  "hashtag": "tesla",
  "hashtagTitle": "tesla",
  "hashtagUrl": "https://www.tiktok.com/tag/tesla",
  "viewCount": 45200000000,
  "videoCount": 12500000,
  "coverImageUrl": null,
  "description": null
}

Output fields

Common fields (all records)

Field	Description
recordType	"video" or "hashtag"
inputType	"searchQuery", "hashtag", or "url"
inputValue	The original input that produced this record
position	Rank/order in discovery results
collectedAt	ISO 8601 timestamp of extraction
sourceUrl	The URL that was scraped
sourceType	"search" or "hashtag"

Video fields

Field	Description
videoId	TikTok video ID
videoUrl	Full video URL
description	Video caption/description
createTime	Video creation time (ISO 8601)
duration	Duration in seconds
region	Region/country code (best-effort, may be null)
language	Content language (if available)
musicId, musicTitle, musicAuthorName	Sound/music info
authorId, authorUniqueId, authorNickname	Author identification
authorProfileUrl, authorAvatarUrl	Author links
authorVerified	Verified badge status
authorFollowerCount, authorHeartCount, authorVideoCount	Author stats (if available)
playCount	Play/view count
likeCount, commentCount, shareCount	Engagement counts
saveCount, repostCount	Save and repost counts (if available)
hashtags	Hashtags used in the caption
mentions	Users mentioned in the caption
thumbnailUrl, coverUrl	Video cover images
isAd, isPinned, isSponsored	Content flags
searchKeyword	The search keyword that found this video (null for hashtag discovery)
matchedHashtag	The hashtag that found this video (null for keyword discovery)
engagementTotal	Sum of likes + comments + shares + saves
engagementRateProxy	engagementTotal / playCount
discoveryScore	Weighted viral score: (shares*3 + saves*2 + comments*1.5 + likes) / plays * 100

Hashtag fields

Field	Description
hashtag	Hashtag name (without #)
hashtagTitle	Display title
hashtagUrl	Full hashtag page URL
viewCount	Total view count
videoCount	Total video count (if available)
coverImageUrl	Cover image URL (if available)
description	Hashtag description (if available)

Use cases

• Content ideation — find trending hashtags and videos in a niche before creating content
• Trend monitoring — track recurring keywords, hashtags, or topics across repeated scheduled runs
• Competitive research — see what types of content are performing around a topic
• Influencer discovery — identify creators associated with emerging trends
• E-commerce/product discovery — detect trending products, categories, and buying signals
• Campaign validation — check whether a hashtag or theme is growing before investing in content or ads
• AI/LLM workflows — feed discovered videos and hashtags into clustering, summarization, trend scoring, and reporting pipelines

Tips and best practices

• Use inputValue to trace results — every record includes the original query or hashtag that produced it, making cross-topic comparison easy
• Start small — use resultsPerQuery: 10–20 for initial exploration, then increase for deeper collection
• Combine inputs — mix searchQueries and hashtags in a single run to compare keyword trends with hashtag trends
• Schedule for monitoring — run on a schedule to track trend momentum over time
• Use discoveryScore — higher scores indicate stronger viral signals (weighted toward shares, saves, and comments)
• Filter by recordType — use the dataset views or filter by recordType to separate video and hashtag records

Proxy configuration

Residential proxies are recommended for reliable TikTok scraping. TikTok applies rate limiting and bot detection that datacenter proxies may not handle well.

{
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Integrations

Connect this actor with other tools and services:

• Google Sheets — export trend data to spreadsheets for team dashboards
• Slack/email — set up notifications when new trends are detected
• Webhooks — trigger downstream workflows when a run completes
• API — access results programmatically via the Apify API
• Other actors — feed discovered video URLs into TikTok Video Scraper for deeper video analysis

FAQ

How many results can I get per query? Up to 500 per keyword or hashtag. The actual number depends on how much data TikTok returns for that query.

Why are some fields null? Fields like region, language, saveCount, and repostCount depend on what TikTok includes in its page data. They are extracted on a best-effort basis.

What is the difference between searchKeyword and matchedHashtag? searchKeyword is populated when a video was discovered via a keyword search. matchedHashtag is populated when discovered via a hashtag page. Use inputValue as a universal field that is always populated regardless of discovery method.

Can I scrape specific video pages? This actor is designed for trend discovery (search + hashtags). For scraping specific video URLs, use TikTok Video Scraper.

How is discoveryScore calculated? discoveryScore = (shares * 3 + saves * 2 + comments * 1.5 + likes) / plays * 100. It weights actions that signal stronger intent (sharing and saving) higher than passive engagement (likes).