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--- |
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language: |
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- multilingual |
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license: mit |
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annotations_creators: |
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- machine-generated |
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language_creators: |
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- found |
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size_categories: |
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- 100M<n<1B |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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- summarization |
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- text-retrieval |
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pretty_name: Exorde Social Media Dataset December 2024 Week 1 |
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tags: |
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- social-media |
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- multi-lingual |
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- sentiment-analysis |
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- emotion-detection |
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- text |
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--- |
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--- |
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# Multi-Source, Multi-Language Social Media Dataset (1 Week Sample) |
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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/. |
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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. |
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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. |
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## Methodology: Total sampling of the web, statistical capture of all topics |
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## Dataset Highlights |
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- **Multi-Source**: Captures content from a wide range of online platforms |
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- **Multi-Language**: Covers 122 different languages |
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- **High-Resolution Temporal Data**: Each entry is timestamped to the exact moment of posting |
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- **Rich Metadata**: Includes sentiment analysis, emotion detection, and thematic categorization |
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- **Large Scale**: 270 million unique entries collected in near real-time |
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- **Diverse Content**: Social media posts, blog entries, news articles, and more |
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## Dataset Schema |
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- **date**: string (exact timestamp of post) |
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- **original_text**: string |
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- **url**: string |
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- **author_hash**: string (SHA-1 hash for privacy) |
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- **language**: string |
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- **primary_theme**: string |
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- **english_keywords**: string |
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- **sentiment**: double |
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- **main_emotion**: string |
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- **secondary_themes**: list<element: int64> |
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## Attributes description |
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- **original_text** is the exact original text of the item/post, as it was collected. It should match the original content before any |
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deletion/edition. |
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- **author_hash** is a SHA-1 Hash of the author username on a given platform, when provided. Many items have None Author_hash. |
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- **language** is detected by a fasttext-langdetect model. Isocode ISO 639. |
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- **primary_theme** is the output of MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33, on on the |
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classes below. |
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- **secondary_themes** are the same theme classes with a mapping: |
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> 1. Economy |
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> 2. Technology |
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> 3. Investing |
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> 4. Business |
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> 5. Cryptocurrency |
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> 6. Social |
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> 7. Politics |
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> 8. Finance |
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> 9. Entertainment |
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> 10. Health |
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> 11. Law |
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> 12. Sports |
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> 13. Science |
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> 14. Environment |
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> 15. People |
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- **main_emotion** is computed from an emotion scoring Language model, fine-tuned on social media data. |
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- **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. |
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- **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. |
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## Key Statistics |
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- **Total entries**: 269,403,210 (543 files, 496138 average rows per file) |
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- **Date range**: 2024-11-14 to 2024-12-11 (included) |
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- **Unique authors**: 21 104 502 |
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- **Languages**: 122 |
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- **Primary themes**: 16 |
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- **Main emotions**: 26 |
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- **Average sentiment**: 0.043 |
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- **Most common emotion**: Neutral |
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### Top 20 Sources |
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- x.com 179,375,295 |
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- reddit.com 52,639,009 |
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- bsky.app 24,893,642 |
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- youtube.com 7,851,888 |
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- 4channel.org 1,077,691 |
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- jeuxvideo.com 280,376 |
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- forocoches.com 226,300 |
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- mastodon.social 225,319 |
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- news.ycombinator.com 132,079 |
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- lemmy.world 120,941 |
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- investing.com 113,480 |
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- tribunnews.com 89,057 |
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- threads.net 55,838 |
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- yahoo.co.jp 54,662 |
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- yahoo.com 38,665 |
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- indiatimes.com 38,006 |
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- news18.com 33,241 |
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- bhaskar.com 30,653 |
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- chosun.com 28,692 |
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- tradingview.com 28,261 |
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- +5000 others |
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[Full source distribution](https://gist.githubusercontent.com/MathiasExorde/53eea5617640487bdd1e8d124b2df5e4/raw/5bb9a4cd9b477216d64af65e3a0918879f806e8b/gistfile1.txt) |
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### Top 10 Languages |
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1. English (en): 190,190,353 |
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2. Spanish (es): 184,04,746 |
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3. Japanese (ja): 14,034,642 |
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4. Portuguese (pt): 12,395,668 |
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5. French (fr): 5,910,246 |
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6. German (de): 4,618,554 |
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7. Arabic (ar): 3,777537 |
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8. Turkish (tr): 2,922,411 |
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9. Italian (it): 2,425,941 |
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[Full language distribution](https://gist.github.com/MathiasExorde/bded85ba620de095705bb20507fcf6f1#file-gistfile1-txt) |
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## About Exorde Labs |
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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: |
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- Current capacity: Processing up to 4 billion elements annually |
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- Growth rate: 20% monthly increase in data volume |
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- Coverage: A comprehensive, real-time snapshot of global online discourse |
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- More than 10 Million data points are processed daily, half a million per hour in near real-time |
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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. |
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For more information about our work and services, visit: |
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- [Exorde Labs Website](https://www.exordelabs.com/) |
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- [Social Media Data](https://www.exordelabs.com/social-media-data) |
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- [Exorde Labs API](https://www.exordelabs.com/api) |
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## Use Cases |
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This dataset is invaluable for a wide range of applications, including but not limited to: |
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- Real-time trend analysis |
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- Cross-platform social media research |
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- Multi-lingual sentiment analysis |
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- Emotion detection across cultures |
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- Thematic analysis of global discourse |
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- Event detection and tracking |
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- Influence mapping and network analysis |
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## Acknowledgments |
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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. |
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Massive thanks to the Exorde Network and its data enthusiast community, unique of its kind. |
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## Licensing Information |
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This dataset is released under the MIT license. |
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## Citation Information |
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If you use this dataset in your research or applications, please cite it as follows: |
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`Exorde Labs. (2024). Multi-Source, Multi-Language Social Media Dataset [Data set]. Exorde Labs. https://www.exordelabs.com/` |
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## Contact Information |
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For questions, feedback, or more information about this dataset or Exorde Labs' services, please contact us at: |
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- Email: [[email protected]](mailto:[email protected]) |
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- Twitter: [@ExordeLabs](https://twitter.com/ExordeLabs) |
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- GitHub: [Exorde Labs](https://github.com/exorde-labs) |
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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. |
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