Instagram Hashtag Scraper

Developed by: @hvgupta and @austinmyc

⚡ What does this Actor do?

This Actor searches Instagram hashtags and extracts:

• Posts from hashtag pages
• Comments on each post
• Replies to comments
• User information (username, content, timestamps)
• Hierarchical data structure showing relationships between posts, comments, and replies

All data is filtered by your specified date range to get only relevant content.

📋 Input Parameters

Parameter	Type	Required	Description	Default
keyword	String	✅	Hashtag keyword to search (without #)	-
hashtag_limit	Integer	❌	Number of hashtags to explore	1
post_limit	Integer	❌	Posts to scrape per hashtag	2
comment_limit	Integer	❌	Comments to scrape per post	10

📊 Output Format

The Actor returns structured JSON data with the following format:

{
  "user_id": "username",
  "datetime": "2024-06-24T10:30:00+08:00",
  "content": "Post or comment content",
  "post_id": "unique_post_identifier",
  "url": "https://instagram.com/p/...",
  "category": "Original Post",
  "current_hash": "abc123def456...",
  "parent_hash": "parent_content_hash_if_applicable"
}

Category Types:

• "Original Post" - Main hashtag posts
• "Comment" - Comments on posts
• "Reply" - Replies to comments

Hash Fields (for hierarchical structure):

• current_hash - hash for deduplication and identification
• parent_hash - Hash of the parent

These hash fields enable the hierarchical relationship structure:

• Original Posts: parent_hash is null
• Comments: parent_hash references the original post's current_hash
• Replies: parent_hash references the parent comment's current_hash

⚙️ How it works

1. Search: Finds hashtags related to your keyword
2. Content Extraction:
   • Opens each hashtag page
   • Extracts posts within your date range
   • Collects comments and replies for each post
   • Handles Instagram's navigation
3. Data Processing: Structures data hierarchically with relationships
4. Output: Returns structured JSON data

📈 Performance & Features

• Smart Navigation: Handles Instagram's dynamic content loading
• Hierarchical Structure: Maintains relationships between posts, comments, and replies
• Memory Efficient: Processes data incrementally to handle large datasets

📝 Example Usage

Using Apify SDK (Python)

from apify_client import ApifyClient

# Initialize the ApifyClient with your API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Prepare the Actor input
run_input = {
    "keyword": "travel",
    "hashtag_limit": 2,
    "post_limit": 5,
    "comment_limit": 15
}

# Run the Actor and wait for it to finish
run = client.actor("YOUR_ACTOR_ID").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset
scraped_output_list = client.dataset(run["defaultDatasetId"]).list_items()
for single_scraped_output in scraped_output_list.items():
  print(single_scraped_output)

Using Apify SDK (JavaScript)

import { ApifyApi } from 'apify-client';

// Initialize the ApifyApi with your API token
const client = new ApifyApi({
    token: 'YOUR_APIFY_TOKEN',
});

// Prepare the Actor input
const input = {
    keyword: "travel",
    hashtag_limit: 2,
    post_limit: 5,
    comment_limit: 15
};

// Run the Actor and wait for it to finish
const run = await client.actor('YOUR_ACTOR_ID').call(input);

// Fetch and log Actor results from the run's dataset
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

Basic JSON Input

{
  "keyword": "travel",
  "hashtag_limit": 2,
  "post_limit": 5,
  "comment_limit": 15
}

inputs to the keyword should not contain any " "s

🔍 Data Quality Features

• Automatic Deduplication: Uses content hashing to prevent duplicate entries
• Input Validation: Validates date ranges and parameter constraints
• Comprehensive Logging: Detailed logs for monitoring and debugging
• Data Integrity: Maintains accurate relationships between posts, comments, and replies
• Time Zone Handling: Properly handles Instagram's timestamp formats

📊 Output Structure

The Actor returns a flat array of JSON objects, but maintains hierarchical relationships through the previously mentioned hash linking:

Example Relationship:

[
  {
    "current_hash": "post123",
    "parent_hash": null,
    "category": "Original Post",
    "content": "Amazing sunset today! #travel",
    "user_id": "traveler_jane",
    "datetime": "2024-06-24T10:30:00+08:00",
    "post_id": "p/ABC123",
    "url": "https://instagram.com/p/ABC123"
  },
  {
    "current_hash": "comment456", 
    "parent_hash": "post123",
    "category": "Comment",
    "content": "Beautiful photo!",
    "user_id": "photo_lover",
    "datetime": "2024-06-24T11:15:00+08:00",
    "post_id": "p/ABC123/comment456",
    "url": "https://instagram.com/p/ABC123"
  },
  {
    "current_hash": "reply789",
    "parent_hash": "comment456", 
    "category": "Reply",
    "content": "I agree, stunning!",
    "user_id": "nature_fan",
    "datetime": "2024-06-24T12:00:00+08:00",
    "post_id": "p/ABC123/comment456/reply789",
    "url": "https://instagram.com/p/ABC123"
  }
]