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.

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.

As of April 2025, 26.42 percent of global Bluesky.com visitors were aged between 25 and 34 years. Additionally, just under one third of users were aged between 18 and 24 years.

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 User Events Stream

This repository contains user events on the Bluesky social network from its inception in Nov. 2022 to July 2023. The events captured include likes, follows, blocks, and other user interactions on the platform. More recent events will be added over time. The data is stored in JSONL format: { "createdAt": "2024-07-21T15:30:00Z", "$type": "app.bsky.feed.like", "did": "did:plc:123456", "uri": "at://did:plc:123456/app.bsky.feed.like/78910", ... //… See the full description on the dataset page: https://huggingface.co/datasets/hallofstairs/bluesky.

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).

Bluesky saw a significant increase in user reports to its moderation service in 2024. The number of reports jumped from 358,000 in 2023 to 6.48 million in 2024, indicating a growing user base and increased platform activity. This surge in moderation reports coincided with a spike in monthly downloads, particularly after the U.S. presidential elections in November 2024, when Bluesky downloads reached 7.35 million. Breakdown of moderation reports The 6.48 million reports submitted to Bluesky's moderation service in 2024 covered various issues. Anti-social behavior accounted for 1.75 million reports, while misleading content and spam received 1.2 million and 1.4 million reports, respectively. These figures suggest that users actively engaged in flagging content that violated platform guidelines. Additionally, Bluesky received 238 requests from law enforcement, governments, and legal entities, responding to 182 of them. The most common legal requests were for user data, followed by takedown requests and inquiries. Comparison with other platforms While Bluesky experienced growth in user reports, other social media platforms like Facebook saw fluctuations in content moderation. In the third quarter of 2024, Facebook removed 6.4 million pieces of hate speech content, down from 7.2 million in the previous quarter. Similarly, Facebook took action on 7.6 million pieces of bullying and harassment related content in the same period, a slight decrease from 7.8 million in the previous quarter. These comparisons highlight the ongoing challenges social media platforms face in content moderation and user safety.

The microblogging platform Bluesky, which launched in February 2023, has been suggested as a Twitter substitute. As people look for a suitable replacement for the text-based social media platform, demand for Bluesky is rising. After the U.S. presidential elections in November 2024, monthly Bluesky downloads surged to *** million. In March 2025, downloads returned to a steady *** million per month.

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 'Human_annotation_on_validation_users.csv' 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 four stances: 1 (favor), 2 (against), 3 (neutral), and 4 (unrelated). To simplify the stance annotations provided by the large language model, the "neutral" and "unrelated" categories are combined and represented as "neither."
• The file 'LLM_annotation_on_validation_users.json' contains stance labels annotated by a state-of-the-art LLM for 445 validation users toward Trump and Harris, resulting in a total of 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_annotation_on_dataset_users.json' is similar to 'LLM_annotation_on_validation_users.json but is generated for 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 'Main_dataset_for_stance_detection.parquet' contains up to 1,000 recent English-language posts (including both original posts and reposts) from each of the 8,022 + 445 = 8,467 users. This file was used for the stance detection task.
• The file 'Bluesky_dataset_on_us_politics.parquet' is similar to 'Main_dataset_for_stance_detection.parquet', but it contains all posts (including both original posts and reposts) from each of the 8,022 + 445 = 8,467 users.
• The file 'Like_network.parquet' captures users' interactions through likes. Specifically, it contains the number of likes each user has given to original posts made by other users. It includes likes from 8,022 + 445 = 8,467 users, but it is not limited to interactions from these users alone.
• The files 'Repost_network.parquet' and 'Quote_network.parquet' are similar to 'Like_network.parquet', but they capture users' interactions through reposts and quotes, respectively.

Historical Dataset of Blue Sky High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),American Indian Student Percentage Comparison Over Years (2000-2023),White Student Percentage Comparison Over Years (1994-2023),Diversity Score Comparison Over Years (1997-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2000-2023)

This dataset is about book subjects. It has 5 rows and is filtered where the books is Applying the Rasch model in social sciences using R and BlueSky statistics. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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.

Code To Reproduce the Analysis of "Bluesky Network Topology, Polarization, and Algorithmic Curation" Data Preparation 1. Unzip DIDs.txt.gz which contains all 4,754,059 valid user DIDs used in this analysis 2. Run the download script with: python download_repos_multip.py --mode all This will download all repositories for the DIDs and store them in the Data/DID_REPO/ folder. 3. Run Code/PythonScripts/data_processing.py to create the SQL database. Main Analysis Data Processing Notebooks 00_CreateMBFC.ipynb - Creates mapping of domains to political stances according to Media Bias Fact Check (MBFC) 01_CreateSQL.ipynb - Creates SQL database and exports upload scripts Analysis Notebooks 02_ActivityOverTime.ipynb - Figure 1: Activity over Time, Table 5: Top Domains 03_TrainTransformer.ipynb - Trains model for stance detection on Israel/Palestine content 04_Stance.ipynb - Figure 9: Proportion of Posts by Stance over Time, Table 1: User Activity Distributions, Figures 2 & 3: Distribution of Interactions, Figures 8 & 10: Heatmap of Ideologies vs Neighborhood Ideology, Figure 7: Distribution of Domains by Ideology 05_TopologyOverTime.ipynb - Figures 4 & 5: Structural Measures over Time 06_Feeds.ipynb - Table 2: Top Feeds on Bluesky, Table 4: Distributions of Feeds, Figure 6: Distribution of Interactions with Feeds 07_TopicModel.ipynb - Table 3: Topic Model of Feeds

Historical Dataset of Bluesky Charter School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2004-2023),Total Classroom Teachers Trends Over Years (2003-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2006-2023),American Indian Student Percentage Comparison Over Years (2004-2023),Asian Student Percentage Comparison Over Years (2004-2023),Hispanic Student Percentage Comparison Over Years (2005-2023),Black Student Percentage Comparison Over Years (2004-2023),White Student Percentage Comparison Over Years (2004-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2004-2023),Free Lunch Eligibility Comparison Over Years (2004-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2004-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

Historical Dataset of Bluesky Virtual Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),White Student Percentage Comparison Over Years (2022-2023)

This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset.

The decentralized social media (DSM) software market is experiencing rapid growth, driven by increasing concerns over data privacy, censorship, and centralized control of online platforms. The market, currently estimated at $500 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 30% from 2025 to 2033, reaching approximately $4 billion by 2033. This expansion is fueled by several key trends: the rising adoption of blockchain technology, the growing demand for user-owned data, and a surge in interest in Web3 applications. The key application segments are personal use and enterprise solutions, with cloud-based deployment leading the way. While the market faces challenges like scalability issues, user experience limitations, and the need for enhanced security measures, the inherent advantages of decentralization – increased transparency, censorship resistance, and improved data security – are powerful drivers of market expansion. The competitive landscape is dynamic, with a multitude of platforms, including Minds, Mastodon, Bluesky, and others, vying for market share. This competition fosters innovation and drives the evolution of the DSM ecosystem, leading to the development of more robust and user-friendly platforms. Regional growth is anticipated across all regions, with North America and Europe expected to maintain a significant market share due to higher technology adoption rates and strong awareness of decentralized technologies. However, rapid adoption in Asia-Pacific and other developing regions is anticipated, potentially leading to a more balanced global distribution in the coming years. The success of the DSM market hinges on overcoming technical and adoption challenges. This includes improving the overall user experience, enhancing interoperability between different platforms, and addressing scalability limitations to support a larger user base. Furthermore, education and awareness campaigns are crucial to attract both individual users and businesses, highlighting the benefits of decentralized social media in terms of privacy, control, and community governance. As the technology matures and the user base expands, the DSM market's growth trajectory is expected to remain strong, potentially transforming the social media landscape as we know it. The integration of innovative features such as decentralized identity solutions and improved content moderation mechanisms will further contribute to the growth and mainstream adoption of these platforms.

Historical Dataset of Bluesky Charter School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (2004-2023),Asian Student Percentage Comparison Over Years (2004-2023),Hispanic Student Percentage Comparison Over Years (2005-2023),Black Student Percentage Comparison Over Years (2004-2023),White Student Percentage Comparison Over Years (2004-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Comparison of Students By Grade Trends

including user-to-user interactions (following and blocking users) and user-to-community interactions (creating and joining communities).

The decentralized social media network (dSMN) market is experiencing substantial growth, driven by increasing concerns over data privacy, censorship, and control by centralized platforms. While precise market sizing data is unavailable, we can infer a significant expansion based on the observed trends and the emergence of various platforms like Mastodon, Bluesky, and others. The Compound Annual Growth Rate (CAGR) likely falls within a range of 25-35% for the forecast period of 2025-2033, reflecting the rising adoption of blockchain technology and the inherent appeal of user-owned data and content. Key drivers include the growing demand for censorship-resistant platforms, enhanced user control over personal information, and the potential for innovative monetization models within the decentralized ecosystem. However, challenges remain, including scalability limitations, user experience complexities, and the need for greater interoperability between different dSMN platforms. The market segmentation encompasses various platform types (e.g., text-based, video-centric, decentralized applications (dApps)), monetization strategies (e.g., token-based rewards, advertising), and technological approaches (e.g., blockchain protocols). Competitive dynamics are characterized by a diverse landscape of established and emerging players, fostering innovation and potential consolidation in the coming years. The future of dSMN hinges on addressing the technological challenges and improving user adoption. Improvements in user experience are crucial for mainstream appeal, while advancements in scalability and security will be essential for sustaining growth. The integration of diverse platforms through interoperability protocols will further enhance the market's potential. The successful adoption of dSMNs will depend on the balance between technological innovation and a user-friendly interface, coupled with effective community building and governance models. While the current market share is fragmented, we project a significant shift towards larger, more established players who effectively combine technology, community engagement, and a clear value proposition for users. The increasing sophistication of decentralized technologies and the evolving regulatory landscape will shape the market’s trajectory in the long term.