Bluesky experienced rapid user growth in late 2024. The platform's user base expanded from 14.5 million in October to 25 million by December, showcasing its increasing popularity among social media users seeking new options. Surge in downloads and user engagement The platform's growth was particularly notable following the U.S. presidential elections in November 2024, when monthly downloads surged to 7.35 million. This increase in user adoption coincided with rising demand for Twitter alternatives. Earlier in the year, Bluesky had already shown strong performance, with 38,000 downloads from Android devices and 30,000 from iOS devices in July 2024. Moderation challenges and user demographics As Bluesky's user base expanded, so did the need for content moderation. In 2024, the platform received 6.48 million reports to its moderation service, a significant increase from 358,000 reports in 2023. These reports included 1.75 million for anti-social behavior, 1.2 million for misleading content, and 1.4 million for spam.

According to a 2024 survey conducted in the United States, daily users of Bluesky were more likely to use YouTube, TikTok, and X daily. Overall, Threads users are more likely to use other Meta Platforms networks every day.

This repository contains the datasets required to reproduce the results presented in the paper "The Rise of Bluesky."

• profile_creations.parquet.zip: Profile creation dates for each user (Fig.1a).
• user_group_date_intervals.pickle.zip: The dates corresponding to each user group (Fig.1a).
• total_engagement_by_day_by_user.parquet.zip: Daily total activity per user group (Fig.1b, d).
• active_user_count_per_day_0_8.parquet.zip: Daily number of active users (Fig.1e, f).
• summary_stats.zip: Files containing daily network statistics such as average degree and node count (Fig.1g).
• group_degree_distributions.zip: Daily user group out-degree distributions (Fig.1g).
• clustering_coef.parquet.zip: Clustering coefficients for follower network for each day (Fig.1h).
• gini_and_kappa.zip: Daily Gini and Kappa statistics (Fig.1i).

Due to its large size, the dataset used to construct the follower network in Fig. 1c is not included here. However, it may be made available upon request under exceptional circumstances.

Bluesky Social Dataset

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue.

The dataset contains the complete post history of over 4M users (81% of all registered accounts), totaling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions.

Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their “like” interactions and time of bookmarking.

Dataset

Here is a description of the dataset files.

• followers.csv.gz. This compressed file contains the anonymized follower edge list. Once decompressed, each row consists of two comma-separated integers representing a directed following relation (i.e., user u follows user v).
• user_posts.tar.gz. This compressed folder contains data on the individual posts collected. Decompressing this file results in a collection of files, each containing the post of an anonymized user. Each post is stored as a JSON-formatted line.
• interactions.csv.gz. This compressed file contains the anonymized interactions edge list. Once decompressed, each row consists of six comma-separated integers representing a comment, repost, or quote interaction. These integers correspond to the following fields, in this order: user_id, replied_author, thread_root_author, reposted_author,quoted_author, and date.
• graphs.tar.gz. This compressed folder contains edge list files for the graphs emerging from reposts, quotes, and replies. Each interaction is timestamped. The folder also contains timestamped higher-order interactions emerging from discussion threads, each containing all users participating in a thread.
• feed_posts.tar.gz. This compressed folder contains posts that appear in 11 thematic feeds. Decompressing this folder results in 11 files containing posts from one feed each. Posts are stored as a JSON-formatted line. Fields are correspond to those in posts.tar.gz, except for those related to sentiment analysis (sent_label, sent_score), and reposts (repost_from, reposted_author);
• feed_bookmarks.csv. This file contains users who bookmarked any of the collected feeds. Each record contains three comma-separated values: the feed name, user id, and timestamp.
• feed_post_likes.tar.gz. This compressed folder contains data on likes to posts appearing in the feeds, one file per feed. Each record in the files contains the following information, in this order: the id of the ``liker'', the id of the post's author, the id of the liked post, and the like timestamp;
• scripts.tar.gz. A collection of Python scripts, including the ones originally used to crawl the data, and to perform experiments. These scripts are detailed in a document released within the folder.

Citation

If used for research purposes, please cite the following paper describing the dataset details:

Andrea Failla and Giulio Rossetti. "I'm in the Bluesky Tonight: Insights from a Year's Worth of Social Data." PlosOne (2024) https://doi.org/10.1371/journal.pone.0310330

Right to Erasure (Right to be forgotten)

Note: If your account was created after March 21st, 2024, or if you did not post on Bluesky before such date, no data about your account exists in the dataset. Before sending a data removal request, please make sure that you were active and posting on bluesky before March 21st, 2024.

Users included in the Bluesky Social dataset have the right to opt-out and request the removal of their data, per GDPR provisions (Article 17).

We emphasize that the released data has been thoroughly pseudonymized in compliance with GDPR (Article 4(5)). Specifically, usernames and object identifiers (e.g., URIs) have been removed, and object timestamps have been coarsened to protect individual privacy further and minimize reidentification risk. Moreover, it should be noted that the dataset was created for scientific research purposes, thereby falling under the scenarios for which GDPR provides opt-out derogations (Article 17(3)(d) and Article 89).

Nonetheless, if you wish to have your activities excluded from this dataset, please submit your request to blueskydatasetmoderation@gmail.com (with the subject "Removal request: [username]"). We will process your request within a reasonable timeframe - updates will occur monthly, if necessary, and access to previous versions will be restricted.

Acknowledgments:

This work is supported by :

• the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”,
  Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu);
• SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021;
• EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).

POLITISKY24 (Political Stance Analysis on Bluesky for 2024) is a first-of-its-kind dataset for stance detection, focused on the 2024 U.S. presidential election. It designed for target-specific user-level stance detection and contains 16,044 user-target stance pairs centered on two key political figures, Kamala Harris and Donald Trump. In addition, this dataset includes detailed metadata, such as complete user posting histories and engagement graphs (likes, reposts, and quotes).

Stance labels were generated using a robust and evaluated pipeline that integrates state-of-the-art Information Retrieval (IR) techniques with Large Language Models (LLMs), offering confidence scores, reasoning explanations, and text spans for each label. With an LLM-assisted labeling accuracy of 81%, POLITISKY24 provides a rich resource for the target-specific stance detection task. This dataset enables the exploration of Bluesky platform, paving the way for deeper insights into political opinions and social discourse, and addressing gaps left by traditional datasets constrained by platform policies.

In the uploaded files:

• The file user_post_history_dataset.parquet includes the posting history of 8,561 active Bluesky users who have shared content related to American politics.

• The file user_post_list_for_stance_detection.parquet contains a list of up to 1,000 recent English-language post IDs per user, intended for use in the stance detection task.

• The file user_network_dataset.parquet captures users’ interactions through likes, reposts, and quotes.

• The file human_annotated_validation_user_stance_dataset.parquet contains human-annotated stance labels for 445 validation users toward Trump and Harris, resulting in a total of 890 user-target pairs. The labels are divided into three stances: 1 (favor), 2 (against), and 3 (neither).

• The file llm_annotated_validation_user_stance_dataset.parquet contains stance labels annotated by an LLM for the same 445 validation users toward Trump and Harris, also totaling 890 user-target pairs. In addition to stance labels, each pair includes an explanation of the reasoning, the source tweets, spans from the source tweets used in the reasoning, and a confidence score.

• The file llm_annotated_full_user_stance_dataset.parquet is similar to the above LLM-annotated validation file but covers all dataset users excluding the validation set. It provides stance labels for 8,022 users toward Trump and Harris, totaling 16,044 user-target pairs.

• The file human_annotated_validation_stance_relevancy_dataset (post-target entity pairs).parquet contains human-annotated stance labels for 175 validation posts toward Trump and Harris, resulting in 350 post-target pairs. The labels are divided into three stances: 1 (favor), 2 (against), and 3 (neither).

• The file human_annotated_validation_stance_relevancy_dataset (query-post stance relevancy pairs).parquet contains 700 query-post stance relevancy pairs derived from the post-target entity pairs.

As of March 2023, decentralized social media platform Mastodon had over ten million registered users. In November 2022, there were 2.5 million users registered to the online network, an increase of around 300 percent within five months. Additionally, Mastodon, which shares similar micro-blogging features to Twitter, gained roughly 500 thousand users within ten days of Elon Musk's Twitter takeover on October 27th, 2022.