mangoesai/MultiStage_MuscariMedia_reddittest113024_related

README: Dataset Cleanup and Processing Journal

Dataset Source

• Original Source: MuscariMedia/reddittest113024
• Dataset Details: Contains 12,000 Reddit posts.

Samplyling

1. Challenges Identified Uneven Data Distribution:

• Months: October and November have similar data amounts, but December has much less.
• Weeks: Significant differences between weeks (e.g., Weeks 42-48 have much more data than Weeks 41, 49, and 50).
• Weekdays vs. Weekends: Weekdays have more data than weekends. Hours: Peaks during certain hours (10 AM - 1 PM, smaller peak 5 PM - 7 PM).
• Non-Numerical Data: Aggregation methods like mean or sum are not applicable.
• Score Interpretation: The score distribution is highly skewed to the right (positive skewness). A small number of titles have very high scores, while the majority have low scores. Median (15) is much lower than the mean (~1,142.78), indicating that most scores are below the mean.

2. Sampling Strategy We are gonna use multi-stage stratified sampling approach with proportional allocation, combined with oversampling in underrepresented strata. This method involves:

a. Stratification Levels: - Level 1: Months (October, November, December) - Level 2: Weeks within each month - Level 3: Weekdays vs. Weekends - Level 4: Hourly intervals b. Alternative Sampling Strategy - To balance the focus on high-scoring titles while maintaining representativeness, consider the following modified approach: Stratified Sampling with Score-Based Selection - Method: Within each stratum, sort the data by score in descending order. Select the top n titles based on score and randomly sample additional titles.

3. Potential Biases and Risks:

• Selection Bias: Overrepresentation of high scores, underrepresentation of diversity.
• Temporal Bias: Skewed comparisons across time periods.

Cleanup and Encoding Tasks

1. Title Text Cleanup:

   • Corrected spelling errors in post titles (e.g., "Presidential Eelction" → "Presidential Election").
   • Removed artifacts and symbols appearing in text strings.
   • Reformatted text for readability using proper quotation marks.

2. Ecode True/False string For columns:

is_crosspostable
can_gild
author_patreon_flair
is_video
stickied
is_original_content
is_self
pinned
no_follow
can_mod_post
spoiler
allow_live_comments
archived
is_reddit_media_domain
author_premium
locked
media_only
is_created_from_ads_ui

3. Encode b'True/False' formate: Handles cases like b'True', b"b'False'", b'true' etc. For columns

over_18
is_meta
is_robot_indexable
quarantine
send_replies
hide_score
contest_mode

4. Encode b'int' formate: For columns:

thumbnail_width
num_comments
pwls

5. permalink: The extra backslashes () you're seeing in fields like permalink, thumbnail, and url are escape characters that appear in the JSON representation of the data. This is a common source of confusion when dealing with JSON strings that contain special characters like forward slashes (/).

The backslashes are not part of the actual string content; they are just there to ensure that the JSON is correctly formatted and can be parsed without errors. For better tracking the link, I added the "https://www.reddit.com" at the front of the "permalink".

Relevance Analysis

• Model: gpt-4o-mini
• Task: Evaluated the relevance of post titles to the US presidential elections.
  • Added a new column us_presidential_election:
    • 1: Title related to US presidential elections.
    • 0: Title not related.

Topic Classification

• Model: gpt-4o-mini
• Added a topics column:
  • Value format: Comma-separated list of topics (e.g., "economy, democracy").
• Possible topics:

   'LGBTQ',
   'Social Security',
   'abortion',
   'age',
   'child care',
   'climate change',
   'conspiracy',
   'crime',
   'democracy',
   'discrimination',
   'disinformation',
   'drug policy',
   'economy',
   'education',
   'election',
   'environment',
   'equity',
   'ethics',
   'foreign relations',
   'freedom of speech',
   'gender',
   'health',
   'human rights',
   'immigrant',
   'justice',
   'media',
   'opportunity',
   'political parties',
   'race',
   'religion',
   'veterans',
   'violence',
   'voting rights',
   'youth'
  

Dataset Restructuring

• Converted the dataset into JSON format with the following structure:
  • Post_Metadata:
    • id, permalink, removed_by_category, created_est, subreddit_subscribers, suggested_sort, is_robot_indexable
  • Author_Data:
    • author, author_full_name, upvote_ratio, author_flair_text
  • Post_Info:
    • title, url, domain, thumbnail, over_18, send_replies, is_original_content
  • Sharing_Info:
    • score, upvote_ratio, num_comments, num_crossposts
  • Additional Fields:
    • us_presidential_election, topics