monitoring-tiktok-mentions-of-brand

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Monitors TikTok for brand mentions and product discussions using apidojo's TikTok scraper on Apify. Triggers when the user asks to: track mentions of a brand on TikTok, monitor TikTok hashtags for brand content, find TikTok videos talking about a product or company, see what TikTok says about a brand this week, track TikTok reactions to a product launch, find TikTok creators who mentioned a competitor brand, monitor brand sentiment on TikTok, or discover viral TikTok content about a specific brand or product. Returns creator handle, views, likes, comments, sentiment signal, and video caption excerpt. Ideal for brand managers, community teams, crisis communications, and product marketing teams.

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Sourceapidojo-io/apidojo-skills

Added on2026-09-03

NPX Install

npx skill4agent add apidojo-io/apidojo-skills monitoring-tiktok-mentions-of-brand

SKILL.md Content

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Monitoring TikTok Mentions of a Brand

Tracks TikTok content mentioning a brand via branded hashtags and keyword searches. TikTok is the fastest-moving platform for brand sentiment — viral criticism or praise can emerge in hours.

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: Run tiktok-scraper for branded hashtags
- [ ] Step 2: Filter by view count and date
- [ ] Step 3: Classify sentiment and content type
- [ ] Step 4: Identify trending posts and crisis signals
- [ ] Step 5: Deliver monitoring report

Step 1: Run the Actor

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

bash

# 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": ["#[BRAND]", "#[BRAND]review", "#[BRAND]honest"],
  "maxItems": 200
}

REST API fallback:

bash

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

Step 2: Classify and Score

Content type:

UNBOXING = "unboxing", "first impression", "first look"
REVIEW = "review", "honest", "real talk", "thoughts on"
COMPLAINT = "disappointed", "refund", "scam", "doesn't work", "returning"
TUTORIAL = "how to", "tips", "tutorial"
ENTERTAINMENT = trend audio, no product focus

Sentiment (lexical — same model as Twitter sentiment skill):

Use positive/negative/neutral indicators
Weight by
```
diggCount
```
(likes) as community agreement signal

Virality signal:

virality = playCount / (follower_count_of_creator + 1)

If

virality > 2.0

: post is reaching well beyond the creator's audience — flag as

TRENDING

Step 3: Crisis Detection

Flag

CRISIS_ALERT

when:

Any single post with
```
playCount > 500K
```
AND
```
sentiment = NEGATIVE
```
More than 5 complaint posts in 48 hours
Comment-to-view ratio > 3% on a negative post (high engagement = controversy)

Step 4: Edge Cases

Hashtag overloaded with unrelated content: Add brand sub-product or model name to narrow
Brand has low TikTok presence (< 10 posts): This is notable data — report it; brand may need proactive TikTok strategy
Duet/Stitch posts about brand: These count as mentions but are often reactions to original content — classify as
```
REACTION
```
and note the source video

Output Format

# TikTok Brand Monitor: [BRAND_NAME]
Period: [DATE_RANGE] | Posts collected: [N] | Total estimated reach: [N] views | Date: [DATE]

## Overall Sentiment
Positive: [X%] | Negative: [X%] | Neutral: [X%]
⚠️ CRISIS ALERTS: [N] (posts above threshold — see below)

## Content Type Distribution
Unboxing: [N] | Reviews: [N] | Complaints: [N] | Tutorials: [N]

## Trending Posts (> 100K Views)
| Creator | @Handle | Views | Likes | Sentiment | Type | Caption Preview |
|---------|---------|-------|-------|-----------|------|----------------|

## CRISIS ALERTS (Negative + High Reach)
| Creator | Views | Complaint Theme | Days Live | Post URL |
|---------|-------|----------------|-----------|---------|

## Top Advocates
| Creator | @Handle | Followers | Views | Post Type | Score |
|---------|---------|-----------|-------|----------|-------|

Troubleshooting

Very few posts found: Brand may be using a different hashtag convention. Try product name without brand (

#[product]

not

#[brand]product

). All results are unboxing/haul content: Normal for consumer brands — this is positive. Set monitoring to alert only on negative content. Crisis alert triggered by troll campaign: Check if complaint posts are from a cluster of new accounts (created within 30 days, < 100 followers) — may be coordinated; note this context in report.