Reddit

Complete guide to fetching Reddit data including subreddit posts and comments.

Endpoints Overview

Endpoint	Description	Credits
`POST /reddit/posts`	Get subreddit posts	1 + 1/post
`POST /reddit/comments`	Get post comments	1 + 0.5/comment

Get Subreddit Posts

Fetch posts from a Reddit subreddit.

Basic Request

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "python",
    "limit": 10
  }'

With Sorting Options

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "machinelearning",
    "sort": "top",
    "period": "week",
    "limit": 25
  }'

All Sort Options

# Hot posts
curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "python", "sort": "hot", "limit": 20}'

# New posts
curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "python", "sort": "new", "limit": 20}'

# Top posts (with time period)
curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "python", "sort": "top", "period": "month", "limit": 20}'

# Rising posts
curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "python", "sort": "rising", "limit": 20}'

# Controversial posts
curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "python", "sort": "controversial", "period": "week", "limit": 20}'

With Pagination

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "python",
    "sort": "new",
    "limit": 25,
    "cursor": "t3_abc123"
  }'

With ML Enrichment

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/posts" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "python",
    "sort": "hot",
    "limit": 20,
    "enrich": ["sentiment", "topics", "intent", "keywords"],
    "categories": {
      "topic": ["question", "tutorial", "showcase", "discussion", "help"]
    }
  }'

Parameters

Parameter	Type	Required	Default	Description
`name`	string	Yes	-	Subreddit name (without r/)
`sort`	string	No	new	Sort: new, hot, top, controversial, rising
`period`	string	No	day	Time period: day, week, month, year, all
`limit`	integer	No	10	Posts to fetch (1-100)
`cursor`	string	No	-	Pagination cursor
`enrich`	array	No	-	ML enrichments
`categories`	object	No	-	Custom topic categories
`llm_provider`	string	No	gemini-2.0-flash	LLM for enrichment

Response

{
  "success": true,
  "data": {
    "posts": [
      {
        "id": "t3_abc123",
        "title": "How do I handle async operations in Python?",
        "post_text": "I'm trying to understand asyncio...",
        "author": "python_learner",
        "url": "https://www.reddit.com/r/python/comments/abc123/",
        "permalink": "/r/python/comments/abc123/how_do_i_handle_async/",
        "created_utc": 1705315800,
        "score": 156,
        "upvote_ratio": 0.94,
        "num_comments": 42,
        "enrichment": {
          "sentiment": "neutral",
          "topics": ["question", "help"],
          "intent": "question",
          "keywords": ["async", "asyncio", "python", "operations"]
        }
      }
    ],
    "cursor": "t3_def456",
    "has_more": true
  },
  "metadata": {
    "credits_used": 41,
    "processing_time": 1.234,
    "enrichments": ["sentiment", "topics", "intent", "keywords"]
  }
}

Get Post Comments

Fetch comments from a Reddit post. Supports both permalink paths and full URLs.

Using Permalink Path

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/comments" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "permalink": "/r/Python/comments/1pilhi2/metacode_the_new_standard/",
    "limit": 50
  }'

Using Full URL

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/comments" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "permalink": "https://www.reddit.com/r/Python/comments/1pilhi2/metacode/",
    "limit": 100
  }'

With ML Enrichment

curl -X POST "https://data-api.deepdiveplatform.com/api/v1/reddit/comments" \
  -H "X-API-Key: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "permalink": "/r/Python/comments/1pilhi2/metacode_the_new_standard/",
    "limit": 100,
    "enrich": ["sentiment", "intent"],
    "categories": {
      "intent": ["support", "criticism", "question", "suggestion"]
    }
  }'

Parameters

Parameter	Type	Required	Default	Description
`permalink`	string	Yes	-	Post permalink or full URL
`limit`	integer	No	50	Comments to fetch (1-500)
`enrich`	array	No	-	ML enrichments
`categories`	object	No	-	Custom topic categories

Response

{
  "success": true,
  "data": {
    "comments": [
      {
        "id": "klm789",
        "author": "helpful_dev",
        "body": "Great question! You should check out the asyncio documentation first, then look at some practical examples.",
        "created_utc": 1705320000,
        "score": 25,
        "enrichment": {
          "sentiment": "positive",
          "sentiment_score": 0.82,
          "intent": "support"
        }
      },
      {
        "id": "nop012",
        "author": "python_expert",
        "body": "I disagree with the approach mentioned. Here's why...",
        "created_utc": 1705321000,
        "score": 15,
        "enrichment": {
          "sentiment": "neutral",
          "sentiment_score": 0.45,
          "intent": "criticism"
        }
      }
    ]
  },
  "metadata": {
    "credits_used": 51,
    "enrichments": ["sentiment", "intent"]
  }
}

Python Examples

Monitor Subreddit Activity

import requests
from datetime import datetime

def monitor_subreddit(subreddit, hours=24):
    all_posts = []
    cursor = None

    while True:
        payload = {
            "name": subreddit,
            "sort": "new",
            "limit": 100,
            "enrich": ["sentiment", "topics"]
        }
        if cursor:
            payload["cursor"] = cursor

        response = requests.post(
            "https://data-api.deepdiveplatform.com/api/v1/reddit/posts",
            headers={"X-API-Key": "your-api-key"},
            json=payload
        )

        data = response.json()
        posts = data["data"]["posts"]

        # Filter by time
        cutoff = datetime.utcnow().timestamp() - (hours * 3600)
        recent_posts = [p for p in posts if p["created_utc"] > cutoff]

        all_posts.extend(recent_posts)

        # Stop if we've gone past the time window
        if len(recent_posts) < len(posts):
            break

        if not data["data"]["has_more"]:
            break

        cursor = data["data"]["cursor"]

    return all_posts

Analyze Discussion Sentiment

import requests
from collections import Counter

def analyze_discussion(permalink, limit=200):
    response = requests.post(
        "https://data-api.deepdiveplatform.com/api/v1/reddit/comments",
        headers={"X-API-Key": "your-api-key"},
        json={
            "permalink": permalink,
            "limit": limit,
            "enrich": ["sentiment", "intent"]
        }
    )

    data = response.json()
    comments = data["data"]["comments"]

    # Aggregate results
    sentiments = Counter(
        c.get("enrichment", {}).get("sentiment", "unknown")
        for c in comments
    )

    intents = Counter(
        c.get("enrichment", {}).get("intent", "unknown")
        for c in comments
    )

    # Calculate average score by sentiment
    sentiment_scores = {}
    for sentiment in ["positive", "negative", "neutral"]:
        matching = [c for c in comments
                   if c.get("enrichment", {}).get("sentiment") == sentiment]
        if matching:
            sentiment_scores[sentiment] = sum(c["score"] for c in matching) / len(matching)

    return {
        "total_comments": len(comments),
        "sentiment_distribution": dict(sentiments),
        "intent_distribution": dict(intents),
        "avg_score_by_sentiment": sentiment_scores
    }

Compare Subreddits

import requests
from collections import Counter

def compare_subreddits(subreddits, posts_per_sub=50):
    results = {}

    for subreddit in subreddits:
        response = requests.post(
            "https://data-api.deepdiveplatform.com/api/v1/reddit/posts",
            headers={"X-API-Key": "your-api-key"},
            json={
                "name": subreddit,
                "sort": "hot",
                "limit": posts_per_sub,
                "enrich": ["sentiment", "topics"]
            }
        )

        data = response.json()
        posts = data["data"]["posts"]

        # Analyze
        sentiments = Counter(
            p.get("enrichment", {}).get("sentiment", "unknown")
            for p in posts
        )

        all_topics = []
        for post in posts:
            topics = post.get("enrichment", {}).get("topics", [])
            all_topics.extend(topics)

        results[subreddit] = {
            "post_count": len(posts),
            "avg_score": sum(p["score"] for p in posts) / len(posts) if posts else 0,
            "avg_comments": sum(p["num_comments"] for p in posts) / len(posts) if posts else 0,
            "sentiment": dict(sentiments),
            "top_topics": Counter(all_topics).most_common(5)
        }

    return results

# Example usage
comparison = compare_subreddits(["python", "javascript", "golang"])

import requests
from collections import Counter
from datetime import datetime

def track_trending_topics(subreddit, days=7):
    response = requests.post(
        "https://data-api.deepdiveplatform.com/api/v1/reddit/posts",
        headers={"X-API-Key": "your-api-key"},
        json={
            "name": subreddit,
            "sort": "top",
            "period": "week",
            "limit": 100,
            "enrich": ["topics", "keywords"]
        }
    )

    data = response.json()
    posts = data["data"]["posts"]

    # Extract all topics and keywords
    all_topics = []
    all_keywords = []

    for post in posts:
        enrichment = post.get("enrichment", {})
        all_topics.extend(enrichment.get("topics", []))
        all_keywords.extend(enrichment.get("keywords", []))

    return {
        "subreddit": subreddit,
        "period": f"last {days} days",
        "posts_analyzed": len(posts),
        "trending_topics": Counter(all_topics).most_common(10),
        "trending_keywords": Counter(all_keywords).most_common(20),
        "credits_used": data["metadata"]["credits_used"]
    }

Introduction

Getting Started

Core Concepts

Platform Guides

Coming Soon

Endpoints Overview

Get Subreddit Posts

Basic Request

With Sorting Options

All Sort Options

With ML Enrichment

Parameters

Response

Get Post Comments

Using Permalink Path

Using Full URL

With ML Enrichment

Parameters

Response

Python Examples

Monitor Subreddit Activity

Analyze Discussion Sentiment

Compare Subreddits

​Endpoints Overview

​Get Subreddit Posts

​Basic Request

​With Sorting Options

​All Sort Options

​With Pagination

​With ML Enrichment

​Parameters

​Response

​Get Post Comments

​Using Permalink Path

​Using Full URL

​With ML Enrichment

​Parameters

​Response

​Python Examples

​Monitor Subreddit Activity

​Analyze Discussion Sentiment

​Compare Subreddits

​Track Trending Topics

Endpoints Overview

Get Subreddit Posts

Basic Request

With Sorting Options

All Sort Options

With Pagination

With ML Enrichment

Parameters

Response

Get Post Comments

Using Permalink Path

Using Full URL

With ML Enrichment

Parameters

Response

Python Examples

Monitor Subreddit Activity

Analyze Discussion Sentiment

Compare Subreddits

Track Trending Topics