---name: discovering-viral-tiktok-content-trendsdescription: >on Apify. Triggers when the user asks to: find trending content formats on TikTok, discoverlicense: Apache-2.0metadata: author: apidojoversion: 1.0apify-actor: apidojo/tiktok-scraper---

Discovering Viral TikTok Content Trends

Identifies what's going viral on TikTok in a niche by analyzing high-performing posts. Extracts repeatable content formats, hooks, and structural patterns — the inputs needed to create content that rides existing momentum.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	TikTok URLs — user profiles, hashtags, music pages, search, locations
keywords	array	Optional	[]	Search keywords/terms to find posts
sortType	string	Optional	RELEVANCE	Sort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED
location	string	Optional	—	ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB)
maxItems	number	Optional	Unlimited	Maximum posts to return across the run
includeSearchKeywords	boolean	Optional	false	Add the matched search keyword field to each post
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search niche hashtags
- [ ] Step 2: Filter for high-performing posts
- [ ] Step 3: Analyze content formats and hooks
- [ ] Step 4: Score trend viability for replication
- [ ] Step 5: Deliver trend brief

Step 1: Search Hashtags

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
  "keywords": ["#[niche]", "#[niche]tiktok", "#[niche]foryou"],
  "maxItems": 200
}

REST API fallback:

curl -X POST   "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"keywords": ["#skincaretips", "#skincaretiktok"], "maxItems": 200}'

Filter: playCount >= min_views

Step 2: Analyze Content Patterns

From caption and available metadata, classify:

hook_type:
  QUESTION = caption starts with or contains "?", "have you", "do you", "did you know"
  LIST = "X things", "here are the", "number [N]"
  BEFORE_AFTER = "I did X for 30 days", "watch the transformation"
  STORY = first-person narrative opener ("I was struggling with...")
  CONTROVERSY = "unpopular opinion", "no one talks about", "this is controversial"
  TUTORIAL = "how to", "step by step", "the only guide you need"

content_length_tier:
  SHORT = video duration < 15 seconds
  MEDIUM = 15-60 seconds
  LONG = > 60 seconds

Step 3: Score Trend Replicability

trend_score = (playCount / 1000000, max 1) * 0.30
            + (diggCount / playCount * 100 > 3 ? 1 : ratio/3) * 0.25
            + (commentCount / playCount * 100 > 0.5 ? 1 : ratio/0.5) * 0.25
            + (format_is_replicable: TUTORIAL/LIST/QUESTION = 1, BEFORE_AFTER = 0.8, STORY = 0.6) * 0.20

Trend window: posts all from the same 7-day period = TRENDING NOW; spread over 30 days = ESTABLISHED FORMAT

Step 4: Edge Cases

• Trend driven by a single mega-creator: If top 3 posts are from same creator, it's a creator trend not a format trend — flag as CREATOR_DRIVEN; harder to replicate without their audience
• Trend requires specific audio: Note if posts share the same sound; audio trends are TikTok-specific and may expire quickly
• Niche too small (< 50 posts above threshold): Lower min_views to 25K; or report the top posts available with a note on data volume
• International content dominates: Filter by caption language; use English-language posts if targeting English-speaking audience

Output Format

# TikTok Viral Trend Report: [NICHE]
Period: [DATE_RANGE] | Posts analyzed: [N] | Viral posts (>[MIN_VIEWS] views): [N] | Date: [DATE]

## Top Performing Formats
| Format | # Posts | Avg Views | Avg Like Rate | Best Example |
|--------|---------|-----------|--------------|-------------|
| Tutorial | [N] | [N] | [X%] | @[handle]: "[caption excerpt]" |
| List | [N] | [N] | [X%] | |

## Top Viral Posts
| Creator | Views | Likes | Comments | Hook Type | Caption Preview |
|---------|-------|-------|----------|----------|----------------|

## Hook Patterns Worth Replicating
1. "[Hook structure]" — used in [N] viral posts, avg [N] views
   Example: "[verbatim hook from top post]"

## Trend Window Assessment
- TRENDING NOW (< 7 days): [N] posts in format
- ESTABLISHED FORMAT (7-30 days): [N] posts
- FADING (> 30 days): [N] posts

## Recommended Content Angles for [NICHE]
1. [Format + hook recommendation with rationale]
2. [Format + hook recommendation]

Troubleshooting

All viral posts are from mega-creators: Add filter follower_count < 500K to surface format trends from creators of all sizes. Format analysis is inconsistent: Caption text is the only metadata available without video analysis; use it as a proxy and note this limitation. Trend is already declining: TikTok trends typically peak and decline within 2-3 weeks; if freshness is low, recommend adapting the format with a new angle rather than copying directly.