monitoring-instagram-brand-mentions

Monitors Instagram for brand mentions and tagged posts using apidojo's Instagram scraper on Apify. Triggers when the user asks to: track Instagram mentions of a brand or product, monitor hashtag activity around a brand on Instagram, find posts where users tag or mention a company on Instagram, discover organic brand sentiment on Instagram, find untagged brand mentions in captions, track user-generated content featuring a brand, or monitor competitor mentions on Instagram. Returns post URL, caption, author handle, likes, comments, timestamp, and mention type. Ideal for brand managers, social listening teams, PR agencies, and community managers.

Tracks all public Instagram posts mentioning a brand — via branded hashtags, @mentions, or product name keywords. Classifies mentions by sentiment and type (UGC, complaint, press coverage, competitor comparison).

Prerequisites

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

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	✅	[]	Instagram URLs — profiles, hashtags, locations, audio pages, reels
until	string	Optional	—	Scrape posts until this date (YYYY-MM-DD)
maxItems	number	Optional	Unlimited	Maximum posts to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

text

Progress:
- [ ] Step 1: Build hashtag and keyword list
- [ ] Step 2: Run instagram-scraper for each hashtag
- [ ] Step 3: Classify mention type and sentiment
- [ ] Step 4: Identify top advocates and critics
- [ ] Step 5: Deliver brand health report

Step 1 & 2: Run instagram-scraper

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~instagram-scraper" \
  --input '{"param": "value"}'

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

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~instagram-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:

text

Tool: apify:run-actor
Actor: "apidojo~instagram-scraper"
Input:
{
  "keywords": ["#[BRAND]", "#[BRAND]review", "#[BRAND]community"],
  "maxItems": 100
}

REST API fallback:

bash

curl -X POST   "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"keywords": ["#[brand]", "#[brand]review"], "maxItems": 100}'

Run for each hashtag cluster. Merge results and deduplicate by postUrl.

Step 2: Classify Mentions

Mention type:

text

UGC = post contains product photo + brand mention; author is not verified
COMPLAINT = caption contains negative indicators: "broken", "disappointed", "scam", "refund", "terrible", "never again"
POSITIVE_REVIEW = caption contains: "love", "amazing", "best", "recommend", "obsessed"
PRESS/EDITORIAL = author is verified OR follower_count > 100K
COMPETITOR_COMPARISON = caption mentions competitor brand alongside this brand

Sentiment: Apply same lexical classification as Twitter sentiment skill (positive/negative/neutral indicators).

Step 3: Score Reach

text

mention_reach = likes + comments * 5 + (followers_of_author / 100)

Step 4: Edge Cases

• Official brand account's own posts in results: Drop posts where ownerUsername = brand's own handle
• Hashtag is overloaded (> 1M posts): Use long-tail branded hashtags instead; or filter by date
• Sentiment misclassified for complex posts: Flag posts with both positive and negative indicators as MIXED; report count
• Foreign language mentions dominant: Report language distribution; flag non-English mentions separately

Output Format

text

# Instagram Brand Mention Monitor: [BRAND]
Posts collected: [N] | Period: [DATE_RANGE] | Date: [DATE]

## Mention Type Distribution
UGC: [N] | Positive Reviews: [N] | Complaints: [N] | Press: [N] | Comparisons: [N]

## Sentiment Summary
Positive: [X%] | Negative: [X%] | Neutral: [X%]
Weighted by reach: Positive [X%] | Negative [X%]

## Top UGC Posts (Most Liked)
| Creator | @Handle | Likes | Type | Caption Excerpt | Post URL |
|---------|---------|-------|------|----------------|---------|

## Complaints to Address
| Creator | Likes | Complaint Summary | Post URL |
|---------|-------|------------------|---------|

## Top Brand Advocates (Most Frequent Positive Posters)
1. @[handle] — [N] positive posts | [N] avg likes

Troubleshooting

Hashtag returns generic posts: The brand hashtag may be ambiguous (e.g. "#apple"). Use #[brand]official or #[brand][product] for precision. Mostly competitor posts: This may indicate your brand is being used in comparison posts — analyze COMPETITOR_COMPARISON category for positioning insights. Sentiment skewed by a single viral negative post: Check weighted sentiment vs. raw sentiment; one viral post can shift the raw numbers.