Bluesky experienced rapid user growth in late 2024. The platform's user base expanded from 14.5 million in October 2024 to 38 million by August 2025, 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.

Launched in February 2023, Bluesky is a microblogging platform that was proposed as a Twitter alternative at the beginning of the year. In July 2024, the Bluesky app generated 38,000 thousand app downloads from Android devices worldwide, while iOS devices generated 30,000 thousand downloads during the month. Bluesky is a semi-decentralized social media, and while users can access the platform only after being invited by members, all internet users can visualize Bluesky posts and content as of the last examined period.

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).

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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), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

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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), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.