monitoring-tiktok-mentions-of-brand
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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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-tiktok-mentions-of-brand- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads monitoring-tiktok-mentions-of-brand automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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_TOKENenvironment 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):
# 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_TOKENmust be set in environment or.envfile.
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:
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 > 500KANDsentiment = 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
REACTIONand 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.
FAQ
What does the monitoring-tiktok-mentions-of-brand skill do?
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.
How do I install monitoring-tiktok-mentions-of-brand?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill monitoring-tiktok-mentions-of-brand in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/monitoring-tiktok-mentions-of-brand/ (personal) or .claude/skills/monitoring-tiktok-mentions-of-brand/ (project).
Which AI agents can use monitoring-tiktok-mentions-of-brand?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is monitoring-tiktok-mentions-of-brand safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains monitoring-tiktok-mentions-of-brand?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 761 recorded installs.
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