--- dataset_info: - config_name: default features: - name: text dtype: string - name: created_at dtype: timestamp[ns, tz=UTC] - name: author dtype: string - name: uri dtype: string - name: has_images dtype: bool - name: reply_to dtype: string splits: - name: train num_bytes: 267405176 num_examples: 1000000 download_size: 179994596 dataset_size: 267405176 - config_name: with-language-predictions features: - name: text dtype: string - name: created_at dtype: timestamp[ns, tz=UTC] - name: author dtype: string - name: uri dtype: string - name: has_images dtype: bool - name: reply_to dtype: string - name: predicted_language dtype: string - name: language_confidence dtype: float64 splits: - name: train num_bytes: 287405176 num_examples: 1000000 download_size: 187712999 dataset_size: 287405176 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: with-language-predictions data_files: - split: train path: with-language-predictions/train-* tags: - bluesky extra_gated_prompt: This dataset contains Bluesky Social data. By accessing this dataset, you agree to adhere to Bluesky's Terms of Service (https://bsky.social/about/support/tos) and use the data responsibly. extra_gated_fields: I agree to comply with Bluesky's Terms of Service and use the data in accordance with their policies: checkbox license: other pretty_name: One Million Bluesky Posts size_categories: - 1M I've removed the data from this dataset since there was a lot of community pushback about its creation/uploading. I will leave the dataset repository up to allow room for discussion of how datasets can be used to help improve Bluesky and allow people to build the tools they need to build their own open models and approaches to creating feeds that work for their needs. Please feel free to continue to leave feedback in the discussions [here](https://huggingface.co/datasets/bluesky-community/one-million-bluesky-posts/discussions). # Dataset Card for 1 Million Bluesky Posts This dataset contains 1 million public posts collected from Bluesky Social's firehose API, intended for machine learning research and experimentation with social media data. The `with-language-predictions` config contains the same data as the default config but with language predictions added using the [glotlid model](https://huggingface.co/cis-lmu/glotlid). ## Dataset Details ### Dataset Description This dataset consists of 1 million public posts from Bluesky Social, collected through the platform's firehose API. Each post contains text content, metadata, and information about media attachments and reply relationships. - **Curated by:** Daniel van Strien - **Language(s) (NLP):** Multiple (primarily English) - **License:** Dataset usage is subject to Bluesky's Terms of Service ### Dataset Sources - **Source:** Bluesky Social firehose API - **Collection Method:** Python script ([get_data.py](get_data.py)) using the [atproto library](https://github.com/MarshalX/atproto) to connect to the firehose API and collect posts ## Uses ### Direct Use This dataset could be used for: - Training and testing language models on social media content - Analyzing social media posting patterns - Studying conversation structures and reply networks - Research on social media content moderation - Natural language processing tasks using social media datas ### Out-of-Scope Use This dataset should not be used for: - Building automated posting systems for Bluesky - Creating fake or impersonated content - Extracting personal information about users - Any purpose that violates Bluesky's Terms of Service ## Dataset Structure The dataset is available in two configurations: ### Default Configuration Contains the following fields for each post: - `text`: The main content of the post - `created_at`: Timestamp of post creation - `author`: The Bluesky handle of the post author - `uri`: Unique identifier for the post - `has_images`: Boolean indicating if the post contains images - `reply_to`: URI of the parent post if this is a reply (null otherwise) ### With Language Predictions Configuration Contains all fields from the default configuration plus: - `predicted_language`: The predicted language code (e.g., eng_Latn, deu_Latn) - `language_confidence`: Confidence score for the language prediction (0-1) Language predictions were added using the [glotlid model](https://huggingface.co/cis-lmu/glotlid) via fasttext, with the process documented in [predict_language.ipynb](predict_language.ipynb). ## Dataset Creation ### Curation Rationale This dataset was created to provide researchers and developers with a large sample of Bluesky posts for machine learning experimentation and social media analysis. ### Source Data #### Data Collection and Processing Posts were collected using a Python script that connects to Bluesky's firehose API using the atproto library. The script: - Processes the real-time feed of public posts - Extracts relevant fields from each post - Saves posts in batches of 100,000 to JSONL files - Includes basic metadata and structural information about each post #### Who are the source data producers? The data comes from public posts made by Bluesky Social users. These users represent a diverse group of individuals and organizations who have chosen to share content publicly on the platform. ### Personal and Sensitive Information The dataset contains public posts and their associated public metadata. While all data is publicly available through Bluesky's API, users should: - Respect user privacy and platform Terms of Service - Not attempt to de-anonymize or aggregate user information - Use the data responsibly and ethically ## Bias, Risks, and Limitations - The dataset represents a snapshot in time and may not reflect current platform usage - Content may be biased towards more active users or specific time periods - Posts are not filtered for content or quality - The dataset may contain biases present in the Bluesky user base - Language distribution may not be representative of all Bluesky users, especially since the posts where collected over a brief period of time when some timezones were asleep - Language predictions are automated and may contain errors, especially for short texts or mixed-language content - The language detection model may have its own biases and limitations in detecting certain languages or scripts