thepowerfuldeez/massive-yt-edu-queue

Massive YouTube Educational Video Queue

Full metadata and content classification for 4,489,228 YouTube educational videos totaling 3,975,157 hours.

Description

This dataset contains metadata, content categorization, and license risk assessment for ~4.5M YouTube videos identified as potentially educational. It serves as the discovery and processing queue for the massive-yt-edu-transcriptions project, which aims to create the world's largest open educational transcript dataset.

Each video has been classified by content type and assessed for license risk using a 3-tier automated classification system.

Collection Methodology

Video Discovery

Videos were discovered through multiple strategies:

• YouTube Search API — Educational keyword queries across dozens of academic disciplines
• Channel crawling — Snowball discovery from known educational channels (universities, MOOCs, conference organizers)
• Related video traversal — Following YouTube's related video graph from known educational content
• Playlist walking — Extracting full playlists from educational channels and course pages
• Quality filter — Minimum 15 minutes duration, 40+ rejection categories to filter non-educational content

Content Classification (3-tier system)

Tier 1: Channel/Source Name Classification

• 207,000+ YouTube channel and playlist names classified via pattern matching
• Patterns cover: universities (500+ institutions worldwide), conferences (100+ series), research institutes, government agencies, corporate talks, coaching/test prep, religious content, gaming/entertainment, medical/health, museums, tech communities
• Each source mapped to content category and license risk level

Tier 2: Title-Based Classification

• For videos without channel metadata, title analysis using regex patterns
• University detection (institution names, course codes, "Lecture N" patterns)
• Conference paper detection (conference names, "Keynote", year patterns)
• Educational keyword detection (tutorials, courses, crash courses)
• Entertainment/gaming detection for exclusion
• Medical/health content detection
• Religious content detection
• Multi-language support (English, Hindi, Russian, Chinese, Japanese, Korean, Arabic)

Tier 3: Priority-Based Fallback

• Videos with educational priority scores (P8-P9) from discovery classified as unclassified_educational
• Remaining unclassifiable content marked as unknown with fair-use-assumed yellow risk

License Risk Assessment

Four risk levels based on content source analysis:

• 🟢 green: Known Creative Commons or public domain license

  • MIT OCW (CC-BY-NC-SA 4.0), Yale OYC (CC-BY-NC-SA), Khan Academy (CC-BY-NC-SA)
  • NPTEL/IIT (CC-BY-SA 4.0), Taiwan OCW (CC-BY-NC-SA)
  • Library of Congress (public domain), NASA, government agencies

• 🟡 yellow: Educational/factual content with strong fair use argument

  • University lectures (factual educational content)
  • Conference talks (meant for public dissemination)
  • Tech talks and corporate presentations
  • Individual educator tutorials
  • Coaching/test prep material

• 🟠 orange: Uncertain, needs individual review

  • Religious content (may be educational but different use case)
  • Non-English content where license couldn't be verified
  • Mixed educational/entertainment channels

• 🔴 red: Non-educational or high-risk content (EXCLUDED from processing queue)

  • Gaming content, entertainment, reactions, drama
  • Music performances, concerts
  • News broadcasts
  • Content clearly not educational

Fair Use Framework

Our transcription project relies on fair use analysis under 17 U.S.C. § 107:

1. Purpose and character of use — Highly transformative: converting audio/video to text for machine learning training and research. The output (text transcripts) serves a fundamentally different purpose than the original (video lectures).
2. Nature of the copyrighted work — Factual/educational content strongly favors fair use. Lectures, tutorials, and conference talks are factual works presenting knowledge.
3. Amount used — Full transcription of audio (weighs against fair use), though only the audio track is used, not video.
4. Effect on market — Text transcripts do not substitute for video content. No one watches a lecture by reading its transcript. The transcript cannot replace the educational experience of the video.

Fields

Field	Type	Description
video_id	string	YouTube video ID (11 characters)
title	string	Video title as listed on YouTube
url	string	Full YouTube URL
duration_seconds	int	Video duration in seconds (0 if unknown)
status	string	Processing status: pending, completed, rejected, error
priority	int	Educational priority score (9=university OCW, 8=lecture, 7=documentary, 5=default)
source	string	Channel name, university, or course identifier
content_category	string	Content classification category (see below)
license_risk	string	License risk level: green, yellow, orange, or red

Statistics

Total: 4,489,228 videos · 3,975,157 hours

Content Categories

Category	Count	Hours	% of Total
unknown	1,249,993	1,088,480	27.8%
coaching_test_prep	829,883	738,964	18.5%
university_lecture	688,191	584,300	15.3%
individual_educator	634,423	611,780	14.1%
unclassified_educational	371,400	300,047	8.3%
non_english_edu	183,846	182,170	4.1%
conference	122,628	125,967	2.7%
gaming_entertainment	65,793	47,052	1.5%
religious	59,033	67,811	1.3%
corporate_talks	56,065	41,388	1.2%
university_ocw	43,335	27,354	1.0%
tech_community	33,379	44,644	0.7%
individual_creator	33,078	12,283	0.7%
medical_health	30,656	22,809	0.7%
research_institute	23,325	24,050	0.5%
government_public	21,521	21,601	0.5%
museum_cultural	14,169	14,785	0.3%
news_media	14,111	13,259	0.3%
mooc_platform	10,480	2,049	0.2%
public_media	3,565	4,046	0.1%
null	432	377	0.0%

License Risk Distribution

Risk	Count	Hours	% of Total
🟡 yellow	4,035,222	3,589,818	89.9%
🟠 orange	319,678	284,616	7.1%
🔴 red	71,815	54,866	1.6%
🟢 green	62,159	45,538	1.4%
⚪ null	367	340	0.0%

Processing Status

Status	Count
pending	4,266,627
rejected	163,307
completed	57,949
error	1,175
timeout	102
processing	81

Priority Distribution

Priority	Count
P9	20,117
P8	1,549,389
P7	11,765
P5	2,819,903
P4	9
P3	12
P0	88,111

Content Category Descriptions

Category	Description
university_lecture	Lectures from identified universities (MIT, Stanford, IITs, etc.)
university_ocw	Official OpenCourseWare with known CC licenses
individual_educator	Independent educators, tutorial creators, online teachers
coaching_test_prep	Test preparation (GATE, JEE, NEET, GRE, etc.) and exam coaching
conference	Academic and tech conference talks (NeurIPS, PyCon, etc.)
corporate_talks	Corporate tech talks, cloud platform tutorials
tech_community	Open source and developer community content
research_institute	Research seminars, colloquia, symposia
medical_health	Medical education, clinical lectures, health content
non_english_edu	Educational content in non-English languages
mooc_platform	MOOC platforms (Coursera, edX channel content)
museum_cultural	Museum lectures, cultural institution content
government_public	Government agencies, public institutions
public_media	Public media educational content
religious	Religious lectures, sermons, scripture study
news_media	News broadcasts, press conferences
gaming_entertainment	Gaming, entertainment (excluded from processing)
individual_creator	General content creators (needs review)
unclassified_educational	High-priority videos without clear category
unknown	Unclassified content, assumed educational

Related Datasets

• massive-yt-edu-transcriptions — Completed transcriptions from this queue

Code

• github.com/thepowerfuldeez/massive_yt_edu_scraper — Scraper and discovery
• github.com/georgethedeveloper77/million-hour-transcription — Classification and transcription pipeline

License

MIT — this metadata dataset. Individual video content has varying licenses as indicated by the license_risk field.