Reddit Comment Pattern Analysis — Model-Agnostic Prompt

Purpose

This file contains a structured prompt you can feed to ANY AI model (GPT-4, Claude, Gemini, Qwen, Llama, etc.) to analyze the same dataset and compare outputs across different intelligence levels.

Data File

The data is in consolidated_data.json (same directory). It contains 1,400 comments from 22 subreddits, collected via Reddit RSS feeds. Comments are ranked by Reddit’s “best” sort algorithm (rank_in_post=1 = top comment). No actual upvote scores are available — use rank_in_post as a proxy for score.

Data Structure

consolidated_data.json
├── metadata (collection info, totals)
└── subreddits
    └── [subreddit_name]
        ├── subreddit, tier, num_posts, num_comments
        └── posts[]
            ├── post_id, post_title, post_author, post_published, post_content
            └── comments[]
                ├── rank_in_post (1 = best/highest-rated comment)
                ├── comment_id, author, body, published
                ├── word_count, ends_with_question

THE PROMPT (copy everything below this line)

You are a Reddit comment performance analyst. I will give you a JSON dataset containing 1,400 comments from 22 subreddits. The comments are ranked by Reddit’s “best” sort algorithm (rank_in_post=1 means it was the top-rated comment on that post). Your job is to analyze what makes top-ranked comments perform better than lower-ranked ones, and produce actionable recommendations for an AI comment generation prompt.

Data Format

Each subreddit has 3-5 top posts (this week’s top)
Each post has up to 25 comments, ranked 1-25 by Reddit’s “best” sort
rank_in_post=1 = highest rated comment on that post
rank_in_post=25 = lowest rated comment collected
No actual upvote scores — use rank as proxy

Analysis Required

Section 1: Per-Subreddit Summary Table

For each of the 22 subreddits, provide:

Number of posts and comments
Average word count of all comments
Average word count of rank-1 comments only
% of comments ending with a question
Dominant comment tone (funny, empathetic, controversial, educational, sarcastic, direct advice, personal story, one-liner, etc.)
Whether rank-1 comments are shorter or longer than average

Section 2: Pattern Analysis

Analyze across ALL 22 subreddits:

Length: Do shorter or longer comments rank higher? What’s the sweet spot per subreddit?
Tone: What tones dominate rank-1 comments vs lower-ranked comments?
Structure: Do top comments follow a pattern? (opinion first → evidence second? personal story → advice? one-liner → pun?)
Questions: Do comments ending with questions rank higher? In which subreddits?
First-mover advantage: Are rank-1 comments always the earliest?
Subreddit categories: Group subreddits by type (conversation, advice, content/visual, news, entertainment, humor) and identify what works in each category.

Section 3: Subreddit Psyche

For each subreddit, describe in 1-2 sentences:

What kind of humor/tone/value the subreddit rewards
What gets downvoted or ignored

Section 4: 10 Qwen Prompt Recommendations

Based on your analysis, provide 10 specific, actionable tweaks to a Qwen Reddit comment generation prompt. Each recommendation should include:

The rule
Why it works (evidence from data)
Which subreddits it applies to

Section 5: Per-Subreddit Comment Templates

For each of the 22 subreddits, provide:

Field	Value
Structure	How the comment should be organized
Length	Word count range
Tone	Emotional register
Example	A sample comment following the template
Do	What to do
Don’t	What to avoid

Output Format

Use TABLES for all data presentation (Markdown pipe tables)
Be concise and data-driven — cite specific numbers from the dataset
Group subreddits by category for easier comparison
Do NOT use phrases like “Great question”, “I hope this helps”, “It’s important to note”
Do NOT hallucinate data — only reference patterns actually present in the JSON

Data

[Paste the contents of consolidated_data.json here, or reference the file path if the model can read files]

Explorer

ANALYSIS_PROMPT