metadata

language:
  - multilingual
license: mit
annotations_creators:
  - machine-generated
language_creators:
  - found
size_categories:
  - 100M<n<1B
source_datasets:
  - original
task_categories:
  - text-classification
  - summarization
  - text-retrieval
pretty_name: Exorde Social Media Dataset December 2024 Week 1
tags:
  - social-media
  - multi-lingual
  - sentiment-analysis
  - emotion-detection
  - text

Multi-Source, Multi-Language Social Media Dataset (1 Week Sample)

This dataset represents a rich, diverse snapshot of global online discourse, collected over nearly one month from November 14, 2024, to December 12, 2024. It comprises 269 million unique social media posts & articles from various social media platforms, blogs, and news articles, all precisely timestamped at the moment of posting. This dataset is procuded by Exorde Labs. www.exordelabs.com/. This dataset includes many conversations around Black Friday, Post US Elections, European financial & political changes, the collapse of the Syrian regime, the killing of the UnitedHealth CEO, and many other topics. The potential is wide. All items in this dataset are captured publicly, in near real-time, allowing post-deletion & retrospective analyses. This dataset is an extract of the full stream produced by Exorde.

Methodology: Total sampling of the web, statistical capture of all topics

Dataset Highlights

Multi-Source: Captures content from a wide range of online platforms
Multi-Language: Covers 122 different languages
High-Resolution Temporal Data: Each entry is timestamped to the exact moment of posting
Rich Metadata: Includes sentiment analysis, emotion detection, and thematic categorization
Large Scale: 270 million unique entries collected in near real-time
Diverse Content: Social media posts, blog entries, news articles, and more

Dataset Schema

date: string (exact timestamp of post)
original_text: string
url: string
author_hash: string (SHA-1 hash for privacy)
language: string
primary_theme: string
english_keywords: string
sentiment: double
main_emotion: string
secondary_themes: list<element: int64>

Attributes description

original_text is the exact original text of the item/post, as it was collected. It should match the original content before any deletion/edition.
author_hash is a SHA-1 Hash of the author username on a given platform, when provided. Many items have None Author_hash.
language is detected by a fasttext-langdetect model. Isocode ISO 639.
primary_theme is the output of MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33, on on the classes below.
secondary_themes are the same theme classes with a mapping:

   1. Economy
   2. Technology
   3. Investing
   4. Business
   5. Cryptocurrency
   6. Social
   7. Politics
   8. Finance
   9. Entertainment
   10. Health
   11. Law
   12. Sports
   13. Science
   14. Environment
   15. People

main_emotion is computed from an emotion scoring Language model, fine-tuned on social media data.
english_keywords is a powerful attribute, computed from an English translation of the original text. These keywords represent the core content (relevant keywords) of the text. They are produced from KeyBert & statistical algorithms. They should be mostly in English except when translation was faulty, in that case they will be in the original language.
Sentiment is computed & aggregated from several models, including deep learning models. It is a value between -1 and 1. -1 being negative, 0 neutral and 1 positive.

Key Statistics

Total entries: 269,403,210 (543 files, 496138 average rows per file)
Date range: 2024-11-14 to 2024-12-11 (included)
Unique authors: 21 104 502
Languages: 122
Primary themes: 16
Main emotions: 26
Average sentiment: 0.043
Most common emotion: Neutral

Top 20 Sources

x.com 179,375,295
reddit.com 52,639,009
bsky.app 24,893,642
youtube.com 7,851,888
4channel.org 1,077,691
jeuxvideo.com 280,376
forocoches.com 226,300
mastodon.social 225,319
news.ycombinator.com 132,079
lemmy.world 120,941
investing.com 113,480
tribunnews.com 89,057
threads.net 55,838
yahoo.co.jp 54,662
yahoo.com 38,665
indiatimes.com 38,006
news18.com 33,241
bhaskar.com 30,653
chosun.com 28,692
tradingview.com 28,261
+5000 others

Full source distribution

Top 10 Languages

English (en): 190,190,353
Spanish (es): 184,04,746
Japanese (ja): 14,034,642
Portuguese (pt): 12,395,668
French (fr): 5,910,246
German (de): 4,618,554
Arabic (ar): 3,777537
Turkish (tr): 2,922,411
Italian (it): 2,425,941

Full language distribution

About Exorde Labs

Exorde Labs is pioneering a novel collective distributed data DePIN (Decentralized Physical Infrastructure Network). Our mission is to produce a representative view of the web, minute by minute. Since our inception in July 2023, we have achieved:

Current capacity: Processing up to 4 billion elements annually
Growth rate: 20% monthly increase in data volume
Coverage: A comprehensive, real-time snapshot of global online discourse
More than 10 Million data points are processed daily, half a million per hour in near real-time

This dataset is a small sample of our capabilities, offering researchers and developers a glimpse into the rich, multi-faceted data we collect and analyze.

For more information about our work and services, visit:

Use Cases

This dataset is invaluable for a wide range of applications, including but not limited to:

Real-time trend analysis
Cross-platform social media research
Multi-lingual sentiment analysis
Emotion detection across cultures
Thematic analysis of global discourse
Event detection and tracking
Influence mapping and network analysis

Acknowledgments

We would like to thank the open-source community for their continued support and feedback. Special thanks to all the platforms and users whose public data has contributed to this dataset.

Massive thanks to the Exorde Network and its data enthusiast community, unique of its kind.

Licensing Information

This dataset is released under the MIT license.

Citation Information

If you use this dataset in your research or applications, please cite it as follows:

Exorde Labs. (2024). Multi-Source, Multi-Language Social Media Dataset [Data set]. Exorde Labs. https://www.exordelabs.com/

Contact Information

For questions, feedback, or more information about this dataset or Exorde Labs' services, please contact us at:

Email: [email protected]
Twitter: @ExordeLabs
GitHub: Exorde Labs

We are committed to supporting the open-source community by providing high-quality, diverse datasets for cutting-edge research and development. If you find this dataset useful, consider exploring our API for real-time access to our full range of social media data.