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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+*.mp3 filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
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+*.webp filter=lfs diff=lfs merge=lfs -text
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diff --git a/README.md b/README.md
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+---
+language:
+  - multilingual
+license: mit
+annotations_creators:
+  - machine-generated
+language_creators:
+  - found
+size_categories:
+  - 10M<n<100M
+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 a one-week period from December 1, 2024, to December 7, 2024. It comprises 65,542,211 entries 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 the period of CyberMonday, Syria regime collapse and UnitedHealth CEO killing & many more topics. The potential is wide.
+All items in this dataset are captured publicly, in near real-time, allowing post-deletion & retrospective analyses.
+
+  
+
+## 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**: 65,542,211 entries collected over just one week
+
+- **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**: 65,542,211
+
+- **Date  range**: 2024-12-01 to 2024-12-07 (included)
+
+- **Unique  authors**: 9,235,952
+
+- **Languages**:  122
+
+- **Primary  themes**: 16
+
+- **Main  emotions**: 26
+
+- **Average  sentiment**: 0.051
+
+- **Most  common emotion**: Neutral
+
+  
+
+### Top 10 Sources
+
+1. x.com: 49,893,080
+
+2. reddit.com: 14,385,495
+
+3. bsky.app: 7,292,262
+
+4. youtube.com: 1,778,020
+
+5. 4channel.org: 233,134
+
+6. jeuxvideo.com: 61,373
+
+7. forocoches.com: 55,683
+
+8. mastodon.social: 54,932
+
+9. news.ycombinator.com: 32,216
+
+  
+
+[Full source distribution](https://gist.githubusercontent.com/MathiasExorde/53eea5617640487bdd1e8d124b2df5e4/raw/5bb9a4cd9b477216d64af65e3a0918879f806e8b/gistfile1.txt)
+
+  
+
+### Top 10 Languages
+
+1. English (en): 53,882,510
+
+2. Spanish (es): 5,012,521
+
+3. Japanese (ja): 3,653,281
+
+4. Portuguese (pt): 3,548,869
+
+5. French (fr): 1,487,180
+
+6. German (de): 1,297,085
+
+7. Turkish (tr): 850,770
+
+8. Arabic (ar): 829,992
+
+9. Italian (it): 720,955
+
+  
+
+[Full language distribution](https://gist.github.com/MathiasExorde/bded85ba620de095705bb20507fcf6f1#file-gistfile1-txt)
+
+  
+
+## 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:
+
+  
+
+- [Exorde Labs Website](https://www.exordelabs.com/)
+
+- [Social Media Data](https://www.exordelabs.com/social-media-data)
+
+- [Exorde Labs API](https://www.exordelabs.com/api)
+
+  
+
+## 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
+
+  
+
+## 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 (1 Week Sample) [Data set]. Exorde Labs. https://www.exordelabs.com/`
+
+  
+
+## 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.
+
+  
+
+## Contact Information
+
+  
+
+For questions, feedback, or more information about this dataset or Exorde Labs' services, please contact us at:
+
+  
+
+- Email: [hello@exordelabs.com](mailto:info@exordelabs.com)
+
+- Twitter: [@ExordeLabs](https://twitter.com/ExordeLabs)
+
+- GitHub: [Exorde Labs](https://github.com/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.
+
+  
+  
+
+![Exorde  Labs Logo](https://cdn.prod.website-files.com/620398f412d5829aa28fbb86/62278ca0202d025e97b76555_portrait-logo-color.png)
+
+  
+  
+
+---
\ No newline at end of file
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