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REVIEW 2 major objections 5 minor 44 references

A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch

T0 review · 2 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Bluesky after its public launch is predominantly left-leaning, low-toxicity, and dominated by original posts, with only 0.08% of posts linking to low-credibility sources.

desk verdict Solid descriptive study of Bluesky's launch with genuinely new empirical findings, but the headline left-leaning majority claim rests on an unreported subset of users and needs a denominator and robustness checks before it can be taken at face value. read the letter →

arxiv 2505.02317 v1 pith:RITX2PVB submitted 2025-05-05 cs.SI

Gianluca Nogara , Erfan Samieyan Sahneh , Matthew R. DeVerna , Nick Liu , Luca Luceri , Filippo Menczer , Francesco Pierri , Silvia Giordano This is my paper · ORCID
classification cs.SI
keywords Blueskydecentralizedsocialmediamisinformationpoliticalleaningtoxicitycontentmoderationlongitudinalanalysissourcecredibility
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to measure what happened to Bluesky when it opened to the public on February 6, 2024, using 56 days of activity data covering 114 million events. It argues that the platform's early community was predominantly left-leaning: 74.61% of users with at least five rated links are classified as left-of-center, while 7.21% are right-of-center. It also argues that misinformation was rare, with low-credibility links in 0.08% of all posts, and that toxicity stayed low across the languages the authors could score. A sympathetic reader would care because these are among the first large-scale measurements of whether a decentralized, user-controlled platform can avoid the misinformation and toxicity problems of centralized networks. The paper further claims that suspicious mass-following accounts appeared at launch, and that many were quickly suspended or flagged, suggesting moderation can work in a decentralized setting.

What carries the argument

The analysis is carried by three measurement instruments applied to the public Firehose event stream. First, every shared domain is labeled with a credibility score and a political-bias score from professional media-rating services; a user's political leaning is the average bias score of the domains they shared, computed only for users who shared at least five rated links. Second, a toxicity classifier scores posts and per-user toxicity in seven languages. Third, a directed reshare network is partitioned with a standard community-detection algorithm to identify the five largest communities, which contain 87% of users. These instruments connect raw activity data to the paper's claims: the credibility labels produce the 0.08% low-credibility figure and the superspreader concentration, the bias labels produce the 74.61% left-leaning majority, and the community partition explains toxicity differences by language and content type.

What would settle it

Take the users classified as left-leaning by their shared domains and compare their leaning with an independent signal, such as the partisan slant of their original post text or their self-declared ideology in bios; if the two measures disagree for a substantial share of users, the 74.61% left-majority claim fails. A simpler check: recompute the user-leaning distribution using only users who wrote original posts with explicit political hashtags, and see whether the left share remains above 70%.

Watch

Extended reading notes

Core claim

The central claim is that Bluesky's first two months as a public platform looked different from mainstream social media in measurable ways, while still reproducing familiar concentration patterns. Using the Firehose stream, the authors find that original posts outnumber reposts, that the follower network's largest strongly connected component more than tripled after the launch, and that users predominantly shared sources rated high in credibility. The headline statistic is the political leaning of the active user base: averaging the political bias scores of the domains each user shared yields 74.61% left-leaning, 18.17% centrist, and 7.21% right-leaning users, with no significant shift before versus after the opening. Misinformation is concentrated in a handful of accounts, with ten users responsible for 62% of low-credibility links, and the authors interpret the quick suspension of several mass-following accounts as evidence that platform moderation was functioning. The overall picture the paper argues for is a left-skewed, comparatively civil, mostly original-content platform whose main risks are manipulation attempts rather than endemic toxicity.

Load-bearing premise

The load-bearing premise is that the average political-bias score of the news domains a user shares is a valid measure of that user's own political leaning, and that users who shared at least five rated links represent the whole Bluesky population; if shared domains do not track personal ideology, the 74.61% left-leaning majority is an artifact of the measurement.

Editorial extensions

If this is right

  • If the early signal persists, Bluesky is a low-misinformation environment: one in roughly 1,250 posts contains a low-credibility link, and high-credibility domains are shared roughly 125 times more often per day.
  • Because only 0.5% of accounts were moderated by November 2024 and most suspicious actors at launch were caught quickly, decentralized moderation can apparently contain coordinated follow-spam without heavy-handed takedowns.
  • A left-skewed user base means Bluesky may become an ideologically homogeneous space, limiting cross-partisan exposure.
  • The concentration of low-credibility sharing in ten accounts implies that removing or demoting a tiny number of superspreaders would cut most misinformation on the platform.
  • Language is a primary organizing axis: Japanese speakers became the largest community after launch, so platform-level statistics may be driven by specific linguistic sub-communities rather than a global culture.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 74.61% figure may overstate the population's left lean if users who share more links are also more left-leaning; the paper only measures users with at least five rated links, and link-sharing itself is behavior, not identity.
  • If moderation decisions correlate with mass-following rather than content, the paper's account-status data could be re-analyzed to test whether suspension was predicted by follow rate, helping future platforms design anti-spam rules.
  • Extending the same pipeline to Bluesky after the 2024 U.S. election and the Brazil migration wave would test whether the left-leaning, low-toxicity profile is stable or a launch-window artifact.
  • Because Japanese posts became 44% of content but toxicity was scored only in seven languages, the overall low-toxicity result might change if Japanese text were scored.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper analyzes 56 days of Bluesky Firehose data (January 9 to March 4, 2024) surrounding the platform's public launch, examining user activity, language use, follower-network structure, political leaning, source credibility, toxicity, communities, and moderation. The main descriptive findings are that Bluesky shows a heavy-tailed activity distribution with a high share of original posts, low overall toxicity, a left-leaning user population, very low prevalence of low-credibility content concentrated among a few 'superspreaders,' and a small fraction of accounts that were later moderated or deleted. The paper interprets these observations as evidence that Bluesky's moderation efforts have been effective and that the platform fosters a different engagement pattern than centralized counterparts.

Significance. If the claims hold, this is one of the first large-scale longitudinal portraits of Bluesky's early public phase and a useful descriptive baseline for decentralized social media. The study's strengths include a large Firehose-based dataset, use of external benchmarks (NewsGuard, Media Bias/Fact Check, Detoxify), explicit discussion of limitations, and a transparent account of data collection. The central claims about low toxicity and high-credibility sharing are well supported by the presented measurements. However, the headline political-leaning claim and the moderation-effectiveness inference rest on assumptions about sample representativeness and temporal interpretation that need additional support before the paper's broader conclusions can be accepted.

major comments (2)
  1. [§4.4, Figure 5, and §5] The manuscript reports that 74.61% of 'active users' are left-leaning, where active users are defined as users who shared at least five posts linking to MBFC-rated websites, but it never reports the number of users in this denominator or its coverage relative to all active users on the platform. Given that only 13.6% of URL-containing posts have MBFC ratings and many users share no news links at all, the denominator could be a small, self-selected group of heavy news sharers. The Discussion's statement that 'Bluesky users lean predominantly left-wing' (Section 5) therefore extends the finding beyond the actually measured subpopulation. Please report the denominator, the coverage rate relative to all active users, and the sensitivity of the 74.61% / 18.17% / 7.21% split to the five-link threshold and to the equal-weight averaging of domains.
  2. [§4.9, Figure 11, and Abstract] The conclusion that Bluesky's 'moderation efforts have been effective' is based on account statuses queried in November 2024, approximately eight months after the observation window. These statuses reflect cumulative enforcement actions and cannot by themselves establish that moderation during or shortly after the study period was effective; they could also reflect later actions or deletions unrelated to the behaviors analyzed. The paper also does not provide a baseline or a discussion of detection bias for the 0.5% moderation rate. Please either temper the effectiveness claim to what the data can support (e.g., 'a small fraction of accounts in our cohort were subsequently moderated or deleted') or add an analysis of time-to-action for the flagged mass-following and low-credibility-sharing accounts.
minor comments (5)
  1. [§4.4] The Mann-Whitney U test comparing pre- and post-opening political leaning is reported only as p = 0.05; please report the exact p-value and state whether any multiple-comparison correction was applied.
  2. [§4.6] There is a typo in 'While the the distributions of toxicity scores'; also, the statement that no threshold was used in §4.6 should be reconciled with the 0.5 toxicity threshold introduced in §4.8.
  3. [§4.9, Figure 11] The sentence 'Fig. 11A suggests that moderation was done against users who violated the terms of service, as users who shared links from low-credibility sources remain' is unclear and appears to contradict the figure description; please rephrase to state what the comparison of account-status groups actually shows.
  4. [§4.9] The text contains a LaTeX formatting artifact: 'AccountDeactivated-emphAccountTakedown' should be 'AccountDeactivated–AccountTakedown' and similarly for the next pair.
  5. [§4.3 and Table 1] The table reports density values such as '4 .4× 10−6' with an unusual space; please format consistently and define whether density is computed on directed or undirected edges.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: conclusions are external-benchmark measurements with stated operational definitions.

full rationale

The paper's central claims are direct empirical measurements of externally labeled data (NewsGuard, Media Bias/Fact Check, Detoxify) and platform activity captured from Bluesky's public Firehose. The political-leaning result is an operational definition: a user's leaning score is the mean MBFC rating of the domains they shared, and the reported 74.61% left-leaning figure is a summary statistic of that measured distribution, not a derivation from an assumed conclusion. The thresholds (e.g., NewsGuard ≤30 for low credibility, ≥5 rated links for 'active users,' toxicity 0.5) are stated, externally sourced choices applied consistently and transparently, with the arbitrariness of the left/center/right cutoff explicitly acknowledged. The self-citation [12] refers to the authors' earlier preliminary report and is used only as prior context; the new analyses rely on newly collected data and independent benchmark ratings, not on that self-citation. The moderation, toxicity, and community findings similarly summarize directly observed actions and computed scores without fitting parameters that are then relabeled as predictions. The acknowledged limitations—short observation window, partial URL rating coverage, and restricted language support for toxicity—are honest scope restrictions rather than hidden circular dependencies. The unreported denominator for the 74.61% figure is a potential generalizability or robustness concern, but not a circularity issue because no fitted input is presented as an independent prediction.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The central claims rest on several externally-sourced ratings and hand-chosen thresholds; none are fitted to the data, but they shape the results. No new entities are introduced.

free parameters (6)
  • NewsGuard low-credibility threshold = <=30
    Chosen following prior work to define low-credibility domains; affects low-credibility sharing rates.
  • NewsGuard high-credibility threshold = >=60
    Chosen following prior work to define mainstream/high-credibility domains; affects the claim that high-credibility sources dominate.
  • Political leaning threshold = <0 left, =0 center, >0 right
    Arbitrary threshold applied to averaged MBFC scores to classify user leaning; directly determines the 74.61% left-leaning claim.
  • Toxicity threshold = 0.5
    Used in community analysis to define a toxic message, following prior work; affects reported toxicity levels.
  • Activity level bins = low 1-9, high 10-99, hyper >100
    Hand-chosen bins for user activity classification in the interplay analysis.
  • Minimum rated links per user = 5
    Users with fewer than 5 links to rated domains are excluded from political leaning analysis; introduces selection bias.
assumptions (7)
  • domain assumption NewsGuard ratings are a valid measure of news source credibility.
    Used in Section 3.2 to label domains; if NewsGuard is biased or incomplete, credibility claims are affected.
  • domain assumption Media Bias/Fact Check ratings are a valid measure of political bias.
    Used in Sections 3.2 and 4.4 to estimate user ideology; external raters may not be neutral.
  • domain assumption The set of domains a user shares is a reliable proxy for their political ideology.
    Central to Section 4.4; users may share articles from sources they disagree with.
  • domain assumption Users with at least five links to rated domains are representative of the active Bluesky population.
    Used in Section 4.4; this subgroup may skew politically, biasing the left-leaning conclusion.
  • domain assumption langdetect accurately classifies the language of posts.
    Used in Section 3.1 to compute language trends; errors could affect English/Japanese shares.
  • domain assumption Detoxify toxicity scores are valid across the seven languages analyzed.
    Used in Section 4.6; model may be less accurate for non-English text, affecting cross-language toxicity comparisons.
  • domain assumption The Firehose collection captures all relevant public activities during the observation period.
    Data collection in Section 3.1; missed activities or reconnection gaps could bias activity and network statistics.

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Cite this review

Pith. "Pith review of A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch." pith.science (2026). https://pith.science/paper/RITX2PVB

@misc{pith2026250502317,
  author       = {Pith},
  title        = {Pith review of: A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RITX2PVB}},
  note         = {Machine review of arXiv:2505.02317}
}
read the original abstract

Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform's public launch, an influx of new users, particularly those posting in English and Japanese, contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky's moderation efforts have been effective.

Figures

Figures reproduced from arXiv: 2505.02317 by the authors.

Figure 1
Figure 1. Bar chart summarizing key dataset statistics over the whole observation pe [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Online activity on Bluesky before and after the public opening (Feb. 6th), [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. (A) Trend of 5 top languages on Bluesky during the observation period. (B) Top 10 languages in Bluesky. Bars of the same color sum to 100% [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Complementary cumulative distributions of node [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Distribution of the average political leaning score of users that shared at least 5 [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Moving average (7-day) of the percentage of high credibility and low-credibility [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Most shared (A) high-credibility and (B) low-credibility websites during the period of analysis. spreading disinformation [36]. Regarding high-credibility domains, a signifi￾cant observation is the presence of German news agencies—e.g., spiegel.de and taz.de—alongside …
Figure 8
Figure 8. Figure 8: Toxicity of Bluesky posts. (A) Density distribution of toxicity for the different languages. The color assignment is indicated in the companion figure. (B) Distributions of the percentages of posts containing toxic content per user, by language. only one post (less tha…
Figure 9
Figure 9. Figure 9: Interplay between metrics by user activity levels. The error bars represent the [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: User-level community analysis. (A) Distributions of percentages of toxic con￾tent. (B) Distributions of political bias, where -3 means extreme left, 0 center, and 3 extreme right. matter. In contrast, communities 2 and 5 are politically focused, with English and Germa…
Figure 11
Figure 11. Figure 11: Analysis of user status categories. (A) Percentage of misinformation shared. (B) Percentage of toxic content shared. except the pairs AccountDeactivated-emphAccountTakedown in both cases and AccountDeactivated-emphInvalidRequest in the case of misinformation. 5. Discu…

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Reference graph

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.