README.md

metadata

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

@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}
}