monitoring-instagram-hashtag-trends

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Monitoring Instagram Hashtag Trends

监控Instagram Hashtag趋势

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.

分析Instagram上hashtag的表现，找出哪些标签能为特定内容品类带来最佳互动效果。基于真实帖子数据构建分层hashtag策略（宽泛/中等/垂直细分）。

Prerequisites

前置条件

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

设置
```
APIFY_TOKEN
```
环境变量
可选：安装Apify MCP服务器

Inputs

输入参数

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

参数	类型	是否必填	默认值	说明
`startUrls`	array	✅	`[]`	Instagram URL — 主页、hashtag、地点、音频页面、Reels
`until`	string	可选	—	抓取截止日期（格式：YYYY-MM-DD）
`maxItems`	number	可选	无限制	返回的最大帖子数量
`customMapFunction`	string	可选	—	用于转换每个输出对象的JavaScript函数

Workflow

工作流程

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 set

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 set

Step 1: Scrape Hashtags

步骤1：抓取Hashtag数据

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

bash

undefined

推荐方式 — run_actor.js（自动处理等待、输出和文件保存）：

bash

undefined

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:**

Tool: apify:run-actor Actor: "apidojo~instagram-scraper" Input: { "keywords": ["[HASHTAG_1]", "[HASHTAG_2]", "..."], "maxItems": 50 }


**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": ["#[hashtag1]", "#[hashtag2]"], "maxItems": 50}'

Run per hashtag (or in batch if MCP supports multiple hashtags in one run).

node scripts/run_actor.js
--actor "apidojo~instagram-scraper"
--input '{"param": "value"}'
--output YYYY-MM-DD_results.json --format json

> `APIFY_TOKEN`必须在环境变量或`.env`文件中配置。

**若Apify MCP可用：**

Tool: apify:run-actor Actor: "apidojo~instagram-scraper" Input: { "keywords": ["[HASHTAG_1]", "[HASHTAG_2]", "..."], "maxItems": 50 }


**REST API备选方案：**
```bash
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}'

针对每个hashtag单独运行（若MCP支持一次运行多个hashtag则可批量处理）。

Step 2: Calculate Metrics

步骤2：计算指标

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

针对每个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)

分数越高，意味着相对竞争环境下的互动表现越好。

Hashtag层级：

HIGH_COMPETITION（高竞争）：帖子数超过100万——难以获得高排名；尽量少用
MID_TIER（中等层级）：帖子数10万至100万——覆盖范围与竞争程度平衡较好
NICHE（垂直细分）：帖子数少于10万——更容易获得高排名，覆盖范围较小但受众更精准

Step 3: Edge Cases

步骤3：异常情况处理

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

Hashtag被封禁：如果抓取返回0条帖子，该hashtag可能已被Instagram封禁——从策略中移除
全新Hashtag（帖子数<1000）：无法计算可靠指标；标记为
```
EMERGING — LOW DATA
```
同一帖子出现在多个Hashtag下：计算互动指标时去重；报告真实的唯一帖子数量

Output Format

输出格式

undefined

undefined

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]

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]

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]

undefined

Avoid: [list of any hashtags that returned 0 results]

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Troubleshooting

故障排除

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.

互动数据波动大：这在Instagram上属于正常现象；使用中位数而非平均值来降低异常值的影响。 Hashtag帖子多但互动量低：高帖子量+低互动量意味着该标签被机器人或垃圾内容占据——对提升覆盖范围价值低；降低优先级。 垂直细分Hashtag自动生成无结果：并非所有垂直领域都有成熟的hashtag社群——专注于已存在且表现良好的标签。