Added dataset card

README.md

@@ -1,37 +1,211 @@
----
-dataset_info:
-  features:
-  - name: timestamp
-    dtype: large_string
-  - name: tweet
-    dtype: large_string
-  - name: language
-    dtype: string
-  - name: total_tokens
-    dtype: int64
-  - name: sentiment_class
-    list:
-    - name: label
-      dtype: string
-    - name: score
-      dtype: float64
-  - name: sentiment_label
-    dtype: string
-  - name: sentiment_score
-    dtype: float64
-  - name: confidence_level
-    dtype: string
-  - name: year
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6772592588.26901
-    num_examples: 22033090
-  download_size: 3584763878
-  dataset_size: 6772592588.26901
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+dataset_info:
+  features:
+  - name: timestamp
+    dtype: large_string
+  - name: tweet
+    dtype: large_string
+  - name: language
+    dtype: string
+  - name: total_tokens
+    dtype: int64
+  - name: sentiment_class
+    list:
+    - name: label
+      dtype: string
+    - name: score
+      dtype: float64
+  - name: sentiment_label
+    dtype: string
+  - name: sentiment_score
+    dtype: float64
+  - name: confidence_level
+    dtype: string
+  - name: year
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 6772592588.26901
+    num_examples: 22033090
+  download_size: 3584763878
+  dataset_size: 6772592588.26901
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+language:
+- en
+tags:
+- Blockchain,
+- DLT
+- Cryptocurrencies
+- Bitcoin
+- Ethereum
+- Crypto
+pretty_name: Distributed Ledger Technology (DLT) / Blockchain Tweets
+size_categories:
+- 10M<n<100M
+---
+# DLT-Tweets
+
+## Dataset Description
+
+### Dataset Summary
+
+DLT-Tweets is a large-scale corpus of social media posts related to Distributed Ledger Technology (DLT). This dataset is part of the larger DLT-Corpus collection, designed to support NLP research, social computing studies, and public discourse analysis in the DLT domain.
+
+The dataset contains **22.03 million social media documents** with **1,120 million tokens** (1.12 billion tokens), spanning posts from **2013 to mid-2023**. All documents are in English and have been processed to protect user privacy.
+
+This dataset is part of the DLT-Corpus collection. For the complete corpus including scientific literature and patent data, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402
+
+### Languages
+
+English (en)
+
+## Dataset Structure
+
+### Data Fields
+
+Each post in the dataset contains the following fields:
+
+- **tweet**: Full text content of the social media post
+- **timestamp**: Date and time when the post was created
+- **year**: Year the post was created
+- **language**: Language code (all entries are 'en' for English)
+- **total_tokens**: Total number of tokens in the tweet
+- **sentiment_class**: Classified sentiment category (e.g., positive, negative, neutral)
+- **sentiment_label**: Detailed sentiment label
+- **sentiment_score**: Numerical sentiment score
+- **confidence_level**: Confidence level of the sentiment classification
+
+**Privacy Protection:** All usernames have been removed from the tweet text to protect user privacy.
+
+### Data Splits
+
+This is a single corpus without predefined splits. Users should create their own train/validation/test splits based on their specific research needs. Consider temporal splits for time-series analyses or studying how discourse evolves over time.
+
+## Dataset Creation
+
+### Curation Rationale
+
+DLT-Tweets was created to address the lack of large-scale social media corpora for studying public discourse, sentiment, and information diffusion in the Distributed Ledger Technology domain. Social media data provides unique insights into:
+
+- Public perception and sentiment toward DLT technologies
+- Real-time reactions to DLT events (market movements, hacks, regulations)
+- Information spreading patterns and viral content
+- Community dynamics and influencer networks
+- The gap between technical development and public understanding
+
+### Source Data
+
+#### Data Collection
+
+Social media posts were aggregated from:
+1. **Previously published academic datasets** focused on cryptocurrency and blockchain topics
+2. **Publicly available industry sources** that collected DLT-related social media data
+
+All data was collected **before Twitter/X's 2023 API restrictions** that limited academic research access. The collection complies with platform Terms of Service that were in effect at the time of collection, which explicitly permitted academic research use.
+
+#### Data Processing
+
+The collection process involved:
+
+1. **Aggregation**: Combining multiple sources while tracking provenance
+2. **Username removal**: Removing all @username mentions to protect privacy
+3. **Duplicate detection**: Identifying and removing duplicate posts
+4. **Language filtering**: Filtering for English-language posts using language detection
+5. **Sentiment analysis**: Adding sentiment labels using automated classification
+6. **Quality filtering**: Removing extremely short posts
+7. **Privacy protection**: Ensuring no identifying information remains
+
+### Annotations
+
+The dataset includes automated sentiment annotations generated using sentiment analysis models. These provide:
+- Sentiment class (positive, negative, neutral)
+- Sentiment scores and confidence levels
+
+#### Annotation Process
+
+Sentiment was determined using automated sentiment analysis models trained on social media text.
+
+### Personal and Sensitive Information
+
+**Privacy Measures:**
+
+- **All usernames have been removed** from the text content
+- No profile information, user IDs, or biographical data is included
+- Only the text content and basic engagement metrics are retained
+- Posts are not linkable back to specific individuals
+
+**Residual Considerations:**
+
+- Some posts may contain self-disclosed information in the text itself
+- Famous individuals or organizations might be identifiable through context
+- Users should not attempt to re-identify individuals from this dataset
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset can enable:
+
+- **Positive impacts**: Understanding public discourse, detecting misinformation, studying information diffusion, analyzing sentiment trends, improving public communication about DLT
+- **Potential negative impacts**: Could be misused for market manipulation, creating targeted misinformation campaigns, or developing manipulative trading systems
+
+**Researchers should implement appropriate safeguards when working with this data.**
+
+### Discussion of Biases
+
+Potential biases include:
+
+- **Platform bias**: Only Twitter/X data is included; other social platforms are not represented
+- **User bias**: Social media users are not representative of the general population
+- **Language bias**: Only English-language posts are included
+- **Temporal bias**: More recent years have more posts due to platform growth and increased DLT interest
+- **Topic bias**: Certain events (market crashes, hacks) may generate disproportionate discussion
+- **Bot bias**: Despite filtering, some bot-generated content may remain
+- **Geographic bias**: English-speaking regions are over-represented
+
+### Other Known Limitations
+
+- **Temporal gap**: No posts after mid-2023 due to platform API restrictions
+- **Context loss**: Username removal eliminates ability to analyze user behavior or influence
+- **Incomplete threads**: Some conversation threads may be incomplete due to filtering
+- **Sarcasm and irony**: Social media posts often use language that is difficult for NLP models to interpret
+- **Misinformation**: Dataset may contain false or misleading claims about DLT
+- **Market sensitivity**: Discussions may reflect market manipulation or coordinated campaigns
+- **Evolving terminology**: DLT terminology evolves rapidly; older posts may use outdated terms
+
+## Additional Information
+
+### Dataset Curators
+
+Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu
+
+### Licensing Information
+
+**CC-BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International)
+
+This dataset is released under CC-BY-NC 4.0 for **research purposes only**. 
+
+**Key terms:**
+- **Attribution required**: You must give appropriate credit to the dataset creators
+- **Non-commercial use**: Commercial use is not permitted under this license
+- **Academic research**: The dataset is intended for academic and non-profit research
+
+**Legal basis:** Data was collected before changes in Twitter/X's Terms of Service in 2023, under terms that explicitly permitted academic research use. See:
+- https://x.com/en/tos/previous/version_18
+- https://x.com/en/tos/previous/version_17
+
+For more information on CC-BY-NC 4.0, see: https://creativecommons.org/licenses/by-nc/4.0/
+
+### Citation Information
+
+```bibtex
+@article{hernandez2025dlt-corpus,
+  title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
+  author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua},
+  year={2025}
+}
+```