apidojo-io/apidojo-skills

monitoring-tiktok-mentions-of-brand

Monitors TikTok for brand mentions and product discussions using apidojo's TikTok scraper on Apify.

Quelltext ansehen
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

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

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]TikTok URLs — user profiles, hashtags, music pages, search, locations
keywordsarrayOptional[]Search keywords/terms to find posts
sortTypestringOptionalRELEVANCESort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED
locationstringOptional—ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB)
maxItemsnumberOptionalUnlimitedMaximum posts to return across the run
includeSearchKeywordsbooleanOptionalfalseAdd the matched search keyword field to each post
customMapFunctionstringOptional—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.

aus demselben Repository

Weitere Skills

Alle Skills
apidojo-io
Community

analyzing-competitor-instagram-content-strategy

Analyzes a competitor's Instagram content strategy and performance using apidojo's Instagram scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on Instagram, benchmark a competitor's Instagram engagement, see what content types perform best for a competitor on Instagram, reverse-engineer a competitor's Instagram content calendar, identify content gaps vs. a competitor on Instagram, compare posting frequency or content themes, or understand why a competitor's Instag

Installationen
3
GitHub Stars
1
Aktualisiert
13. Mai
apidojo-io
Community

scraping-tiktok-user-data

Scrapes detailed TikTok user data and recent video history using apidojo's TikTok User scraper on Apify. Triggers when the user asks to: get all videos from a TikTok account, fetch a TikTok creator's recent posts and stats, export a TikTok user's video history, scrape all TikTok content from a specific creator, get TikTok post data by username, or collect video-level metrics from a TikTok profile. Returns per-video view count, likes, comments, shares, captions, and timestamps for a given account

Installationen
3
GitHub Stars
1
Aktualisiert
13. Mai
apidojo-io
Community

monitoring-trending-topics-twitter-by-niche

Monitors trending topics and conversations in a specific niche on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: monitor trending topics in a niche on Twitter, track what's being discussed in an industry on X right now, find emerging conversations in a sector on Twitter, see what topics are trending in a specific community, track real-time buzz around a business topic on Twitter, or identify breaking trends before they hit mainstream media. Returns trending topic

Installationen
3
GitHub Stars
1
Aktualisiert
13. Mai
apidojo-io
Community

scraping-twitter-list-members

Scrapes all members and their profile data from any public Twitter/X list using apidojo's Twitter List scraper on Apify. Triggers when the user asks to: get all members of a Twitter list, export accounts in a Twitter list, scrape profiles from a curated Twitter list, fetch the usernames in a public Twitter list, download data from a Twitter list URL, or collect profile stats for everyone on a list. Returns username, bio, follower count, and profile metadata for each list member. Ideal for compet

Installationen
2
GitHub Stars
1
Aktualisiert
13. Mai