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Monitoring Instagram Hashtag Trends
Analyzes hashtag performance on Instagram to identify which tags drive the best engagement for a content category. Builds a tiered hashtag strategy (broad/mid/niche) based on actual post data.
Prerequisites
APIFY_TOKENenvironment 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
Progress:
- [ ] Step 1: Scrape recent posts for each hashtag
- [ ] Step 2: Calculate per-hashtag metrics
- [ ] Step 3: Tier hashtags by competition/opportunity
- [ ] Step 4: Build recommended hashtag setStep 1: Scrape 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~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 jsonAPIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~instagram-scraper"
Input:
{
"keywords": ["[HASHTAG_1]", "[HASHTAG_2]", "..."],
"maxItems": 50
}REST API fallback:
curl -X POST "https://api.apify.com/v2/acts/apidojo~instagram-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{"keywords": ["#[hashtag1]", "#[hashtag2]"], "maxItems": 50}'Run per hashtag (or in batch if MCP supports multiple hashtags in one run).
Step 2: Calculate Metrics
For each hashtag:
avg_likes = mean(likesCount for all sampled posts)
avg_comments = mean(commentsCount for all sampled posts)
engagement_per_post = avg_likes + avg_comments
post_volume_estimate = total posts shown (from platform, if available)
opportunity_score = engagement_per_post / (post_volume_estimate / 10000 + 1)Higher score = better engagement relative to competition.
Hashtag tier:
- HIGH_COMPETITION: > 1M posts — hard to rank; use rarely
- MID_TIER: 100K–1M posts — good reach/competition balance
- NICHE: < 100K posts — easier to rank, less reach but more targeted
Step 3: Edge Cases
- Hashtag is banned: If scrape returns 0 posts, hashtag may be banned by Instagram — drop from strategy
- Very new hashtag (< 1K posts): Can't calculate reliable metrics; flag as
EMERGING — LOW DATA - Same posts appear across multiple hashtags: Deduplicate when calculating engagement metrics; report true unique post count
Output Format
# Instagram Hashtag Strategy: [NICHE]
Hashtags tested: [N] | Date: [DATE]
## Performance by Hashtag
| Hashtag | Est. Posts | Avg Likes/Post | Avg Comments | Tier | Opportunity Score |
|---------|-----------|---------------|-------------|------|-----------------|
| #[tag] | [N] | [N] | [N] | MID_TIER | [0.XX] |
## Recommended Hashtag Set (Mix Strategy)
Use 20-30 hashtags per post in this ratio:
- 5 HIGH_COMPETITION tags: [list]
- 10 MID_TIER tags: [list]
- 10 NICHE tags: [list]
## Banned / Restricted Hashtags
Avoid: [list of any hashtags that returned 0 results]Troubleshooting
Engagement data varies widely: Normal for Instagram; use median, not mean, to reduce outlier impact. Hashtag has many posts but low engagement: High volume + low engagement = dominated by bots or spam — low-value for reach; deprioritize. Niche hashtag auto-generation produces no results: Not all niches have well-established hashtag communities — focus on the ones that exist and perform.
