pith. sign in

arxiv: 2512.24351 · v2 · submitted 2025-12-30 · 💻 cs.CY

Effects of Algorithmic Visibility on Conspiracy Communities: Reddit after Epstein's 'Suicide'

Asja Attanasio , Francesco Corso , Gianmarco De Francisci Morales , Francesco Pierri This is my paper

Pith reviewed 2026-05-16 18:58 UTC · model grok-4.3

classification 💻 cs.CY
keywords algorithmic visibilityconspiracy communitiesRedditlinguistic similarityselection mechanismsurvival analysisonline discoursecommunity composition
0
0 comments X

The pith

A visibility shock draws short-term users who stay distant from core conspiracy discourse rather than integrating as lasting members.

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

The paper studies the effects of a sudden mainstream visibility surge on Reddit's r/conspiracy subreddit after Jeffrey Epstein's death. It compares users who joined at different points during the event using measures of linguistic similarity, semantic distance, toxicity, and participation duration over twelve months. The central finding is that users arriving at peak visibility participate more briefly and remain linguistically distant from established members, while earlier joiners align more closely and engage longer. This pattern points to visibility acting through a selection process that filters for transient participants rather than simply amplifying existing community activity. A reader would care because the result challenges assumptions that external attention automatically expands and deepens such spaces.

Core claim

Following the death of Jeffrey Epstein, the subreddit r/conspiracy experienced a significant visibility shock that brought mainstream users into direct contact with established conspiracy narratives. Users who join during the arrest period tend to show higher linguistic similarity to core users and more stable engagement over time. By contrast, users who arrive during the height of public visibility remain semantically distant from core discourse and participate more briefly. Overall, mainstream visibility connects with changes in audience size, community composition, and linguistic cohesion, but incidental exposure during attention shocks does not typically produce durable, integrated社区成员.

What carries the argument

Comparison of joining timing during the visibility shock, tracked through survival analysis combined with lexical and semantic similarity measures to separate selection from amplification effects.

If this is right

  • Community size grows during visibility shocks but with a shift toward less integrated participants.
  • Linguistic cohesion within the subreddit declines as semantically distant users enter at peak attention.
  • Incidental mainstream exposure rarely converts into long-term, engaged members of the conspiracy space.
  • Platform recommendation systems influence community evolution by modulating the mix of transient versus stable users.
  • External events reshape conspiracy communities mainly through selective entry rather than uniform amplification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platform designers could test gradual rather than sudden visibility increases to encourage deeper integration in polarized spaces.
  • The same timing-based selection pattern may appear in other high-attention online communities such as political or health discussions.
  • Tracking participants beyond the twelve-month window could show whether brief visitors exert indirect influence through comments or votes before leaving.
  • Moderation efforts might prioritize content quality for new arrivals during spikes instead of focusing only on overall volume.

Load-bearing premise

That differences in when users joined during the visibility period can be read as the direct causal impact of mainstream exposure without major interference from the specific content of the Epstein events or other external factors.

What would settle it

Finding that peak-visibility joiners show linguistic similarity and participation lengths statistically indistinguishable from arrest-period or organic users, after matching on topic exposure.

Figures

Figures reproduced from arXiv: 2512.24351 by Asja Attanasio, Francesco Corso, Francesco Pierri, Gianmarco De Francisci Morales.

Figure 1
Figure 1. Figure 1: Longitudinal cohort analysis showing weekly posting activity for each of the four user cohorts. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Longitudinal Google Search prevalence of keywords: ‘Jeffrey Epstein’ and ‘Reddit’ during the arrest and death [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Interrupted time series analysis of core users’ toxicity. The blue line shows the observed daily toxicity estimate. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Shift in Empath category of core users’ posts spanning the arrest and the death of Jeffrey Epstein in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Shift in Empath category between core and new users’ posts spanning the arrest and the death of Jeffrey Epstein [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Kaplan-Meier survival curves for the four newcomer cohorts. From top to bottom over most of the time range, the [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Temporal evolution of semantic alignment for new user cohorts, measured as cosine distance from weekly core [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
read the original abstract

Following the death of Jeffrey Epstein, the subreddit r/conspiracy experienced a significant visibility shock that brought mainstream users into direct contact with established conspiracy narratives. In this work, we explore how large-scale surges in public attention reshape participation and discourse within online conspiracy communities. We ask whether a sudden increase in exposure changes who join r/conspiracy, how long they stay, and how they adapt linguistically, compared with users who arrive through organic discovery. Using a computational framework that combines toxicity scores, survival analysis, and lexical and semantic measures over a period of 12 months, we observe that mainstream visibility is is associated with patterns consistent with a selection mechanism rather than a simple amplifier. Users who join the conspiracy community during the arrest-period tend to show higher linguistic similarity to core users, especially regarding linguistic and thematic norms and showing more stable engagement over time. By contrast, users who arrive during the height of public visibility remain semantically distant from core discourse and participate more briefly. Overall, we find that mainstream visibility is connected with changes in audience size, community composition, and linguistic cohesion. However, incidental exposure during attention shocks does not typically produce durable, integrated community members. These results provide a more nuanced understanding of how external events and platform visibility influence the growth and evolution of conspiracy spaces, offering insights for the design of responsible and transparent recommendation systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper examines the effects of a visibility shock on the r/conspiracy subreddit following Jeffrey Epstein's death. Using a 12-month dataset and a computational framework combining toxicity scores, survival analysis, and lexical/semantic similarity measures, it compares users joining during the arrest period versus the height of public visibility. The central claim is that mainstream visibility triggers a selection mechanism rather than simple amplification: arrest-period joiners show higher linguistic similarity to core users and longer engagement, while peak-visibility joiners remain semantically distant and participate more briefly. The authors conclude that incidental exposure does not produce durable, integrated community members and discuss implications for recommendation systems.

Significance. If the observational patterns can be substantiated with statistical controls and operational details, the work would offer a nuanced contribution to computational social science on how algorithmic visibility reshapes conspiracy communities, distinguishing selection from amplification effects. It could inform platform design for responsible recommendations. However, the absence of sample sizes, tests, and confounder controls in the current version limits the strength of the evidence and the reliability of the causal interpretation.

major comments (3)
  1. [Methods] Methods section: The arrest-period and peak-visibility cohorts are defined using an external event but without explicit temporal boundaries, inclusion criteria, or controls for event-phase content (e.g., initial arrest news vs. later visibility surges). This leaves the comparison vulnerable to confounding by self-selection into the event content itself rather than isolating algorithmic visibility.
  2. [Results] Results section: No sample sizes, cohort sizes, statistical tests (e.g., p-values, effect sizes for linguistic similarity or survival differences), or confidence intervals are reported for the claimed patterns in toxicity, engagement duration, or semantic distance. This prevents assessment of whether the data support the selection-mechanism claim over noise or alternative explanations.
  3. [Results] Results/Discussion: The survival analysis and linguistic measures lack details on operationalization (e.g., how semantic similarity is computed, handling of censoring in survival models, or inclusion of covariates for user traits or concurrent news cycles), undermining the ability to evaluate the robustness of the finding that peak-visibility users participate more briefly.
minor comments (2)
  1. [Abstract] Abstract: Typo 'mainstream visibility is is associated' should read 'is associated'.
  2. [Methods] The 12-month period and exact data collection window should be stated with precise start/end dates in the Methods section for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript examining the effects of algorithmic visibility on r/conspiracy following Epstein's death. We address each of the major comments below and commit to a major revision incorporating the suggested improvements to enhance the methodological transparency and statistical rigor of our findings.

read point-by-point responses

  1. Referee: [Methods] Methods section: The arrest-period and peak-visibility cohorts are defined using an external event but without explicit temporal boundaries, inclusion criteria, or controls for event-phase content (e.g., initial arrest news vs. later visibility surges). This leaves the comparison vulnerable to confounding by self-selection into the event content itself rather than isolating algorithmic visibility.

    Authors: We agree that the temporal boundaries and controls need clarification to strengthen the causal interpretation. In the revised manuscript, we will explicitly define the arrest-period and peak-visibility periods using the external event timelines, specify inclusion criteria for users (e.g., first post during the period), and incorporate controls such as a pre-shock baseline cohort to address self-selection and content confounding. revision: yes

  2. Referee: [Results] Results section: No sample sizes, cohort sizes, statistical tests (e.g., p-values, effect sizes for linguistic similarity or survival differences), or confidence intervals are reported for the claimed patterns in toxicity, engagement duration, or semantic distance. This prevents assessment of whether the data support the selection-mechanism claim over noise or alternative explanations.

    Authors: We acknowledge the need for statistical reporting. The revised paper will include cohort sizes, report all relevant statistical tests with p-values, effect sizes, and confidence intervals for the differences in toxicity, engagement duration, and semantic similarity measures. revision: yes

  3. Referee: [Results] Results/Discussion: The survival analysis and linguistic measures lack details on operationalization (e.g., how semantic similarity is computed, handling of censoring in survival models, or inclusion of covariates for user traits or concurrent news cycles), undermining the ability to evaluate the robustness of the finding that peak-visibility users participate more briefly.

    Authors: We will provide detailed operationalization in the Methods section of the revision. This includes specifying the computation of semantic similarity (e.g., using embedding models), how censoring is handled in survival analysis, and the inclusion of relevant covariates such as user traits and news cycle indicators. revision: yes

Circularity Check

0 steps flagged

No circularity: external event defines independent cohorts

full rationale

The paper performs an observational comparison of user cohorts defined by joining time relative to the external Epstein event. Survival analysis, toxicity scoring, and semantic similarity measures are applied to independent groups (arrest-period vs. peak-visibility joiners) without any fitted parameter being reused as a prediction, without self-definitional quantities, and without load-bearing self-citations that close the derivation. The central claims rest on direct contrasts between externally timed cohorts rather than on any internal reduction of outputs to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no free parameters, axioms, or invented entities are described. The analysis relies on standard computational social science techniques whose details are not provided.

pith-pipeline@v0.9.0 · 5549 in / 1275 out tokens · 57907 ms · 2026-05-16T18:58:44.709370+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · 1 internal anchor

  1. [1]

    Americans Say Murder More Likely Than Suicide in Epstein Case

    2019. Americans Say Murder More Likely Than Suicide in Epstein Case. Rasmussen Reports®. https://web.archive.org/web/20200519051748/https: //www.rasmussenreports.com/public_content/politics/general_politics/august_2019/americans_say_murder_more_likely_than_suicide_in_ epstein_case

    work page arXiv 2019

  2. [2]

    Duilio Balsamo, Paolo Bajardi, Gianmarco De Francisci Morales, Corrado Monti, and Rossano Schifanella. 2023. The Pursuit of Peer Support for Opioid Use Recovery on Reddit. InInternational AAAI Conference on Web and Social Media (ICWSM, Vol. 17). 12–23. doi:10.1609/icwsm.v17i1.22122

    work page doi:10.1609/icwsm.v17i1.22122 2023

  3. [3]

    Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020. The Pushshift Reddit Dataset.Proceedings of the International AAAI Conference on Web and Social Media14 (May 2020), 830–839. doi:10.1609/icwsm.v14i1.7347

    work page doi:10.1609/icwsm.v14i1.7347 2020

  4. [4]

    Alessandro Bessi, Mauro Coletto, George Alexandru Davidescu, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. 2015. Science vs conspiracy: Collective narratives in the age of misinformation.PloS one10, 2 (2015), e0118093

    work page 2015

  5. [5]

    Francesco Corso, Francesco Pierri, and Gianmarco De Francisci Morales. 2025. Conspiracy Theories and Where to Find Them on TikTok. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (Eds.). Association for Computation...

    work page doi:10.18653/v1/2025.acl-long.408 2025

  6. [6]

    Francesco Corso, Giuseppe Russo, Francesco Pierri, and Gianmarco De Francisci Morales. 2025. Early linguistic fingerprints of online users who engage with conspiracy communities.arXiv preprint arXiv:2506.05086(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  7. [7]

    Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. InProceedings of the 22nd international conference on World Wide Web. ACM, Rio de Janeiro Brazil, 307–318. doi:10.1145/2488388.2488416

    work page doi:10.1145/2488388.2488416 2013

  8. [8]

    Lars de Wildt and Stef Aupers. 2024. Participatory conspiracy culture: Believing, doubting and playing with conspiracy theories on Reddit. Convergence30, 1 (Feb. 2024), 329–346. doi:10.1177/13548565231178914 Publisher: SAGE Publications Ltd

    work page doi:10.1177/13548565231178914 2024

  9. [9]

    Karen M Douglas and Robbie M Sutton. 2023. What are conspiracy theories? A definitional approach to their correlates, consequences, and communication.Annual review of psychology74, 1 (2023), 271–298

    work page 2023

  10. [10]

    Emma Grey Ellis. [n. d.]. ‘Epstein Didn’t Kill Himself’ and the Meme-ing of Conspiracy. Wired.Wired([n. d.]). https://www.wired.com/story/epstein- didnt-kill-himself-conspiracy/ Section: tags

    work page

  11. [11]

    Ethan Fast. 2025. Ejhfast/empath-client. Github. https://github.com/Ejhfast/empath-client. https://github.com/Ejhfast/empath-client original-date: 2016-04-16T23:56:42Z

    work page 2025

  12. [12]

    Manish Kumar Goel, Pardeep Khanna, and Jugal Kishore. 2010. Understanding survival analysis: Kaplan-Meier estimate.International journal of A yurveda research1, 4 (2010), 274

    work page 2010

  13. [13]

    Laura Hanu and team Unitary. 2020. Detoxify. Github. https://github.com/unitaryai/detoxify. doi:10.5281/zenodo.7925667

    work page doi:10.5281/zenodo.7925667 2020

  14. [14]

    Manoel Horta Ribeiro, Homa Hosseinmardi, Robert West, and Duncan J Watts. 2023. Deplatforming did not decrease Parler users’ activity on fringe social media.PNAS Nexus2, 3 (March 2023), pgad035. doi:10.1093/pnasnexus/pgad035

    work page doi:10.1093/pnasnexus/pgad035 2023

  15. [15]

    Julian Kauk, Helene Kreysa, and Stefan R Schweinberger. 2021. Understanding and countering the spread of conspiracy theories in social networks: Evidence from epidemiological models of Twitter data.Plos one16, 8 (2021), e0256179

    work page 2021

  16. [16]

    Colin Klein, Peter Clutton, and Adam G. Dunn. 2019. Pathways to conspiracy: The social and linguistic precursors of involvement in Reddit’s conspiracy theory forum.PLOS ONE14, 11 (Nov. 2019), e0225098. doi:10.1371/journal.pone.0225098 Publisher: Public Library of Science

    work page doi:10.1371/journal.pone.0225098 2019

  17. [17]

    Johannes Langguth, Daniel Thilo Schroeder, Petra Filkuková, Stefan Brenner, Jesper Phillips, and Konstantin Pogorelov. 2023. Coco: an annotated twitter dataset of covid-19 conspiracy theories.Journal of Computational Social Science6, 2 (2023), 443–484

    work page 2023

  18. [18]

    Yuanyuan Lei and Ruihong Huang. 2023. Identifying conspiracy theories news based on event relation graph.arXiv preprint arXiv:2310.18545 (2023)

    work page arXiv 2023

  19. [19]

    Corrado Monti, Matteo Cinelli, Carlo Valensise, Walter Quattrociocchi, and Michele Starnini. 2023. Online conspiracy communities are more resilient to deplatforming.PNAS Nexus2, 10 (Oct. 2023), pgad324. doi:10.1093/pnasnexus/pgad324

    work page doi:10.1093/pnasnexus/pgad324 2023

  20. [20]

    Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, and Gianluca Stringhini. 2021. Soros, child sacrifices, and 5G: understanding the spread of conspiracy theories on web communities.arXiv preprint arXiv:2111.02187(2021)

    work page arXiv 2021

  21. [21]

    Shruti Phadke, Mattia Samory, and Tanushree Mitra. 2022. Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions.Proceedings of the International AAAI Conference on Web and Social Media16 (May 2022), 770–781. doi:10.1609/icwsm.v16i1.19333 16 Attanasio et. al

    work page doi:10.1609/icwsm.v16i1.19333 2022

  22. [22]

    Nicholas Proferes, Naiyan Jones, Sarah Gilbert, Casey Fiesler, and Michael Zimmer. 2021. Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics.Social Media + Society7, 2 (April 2021), 20563051211019004. doi:10.1177/20563051211019004 Publisher: SAGE Publications Ltd

    work page doi:10.1177/20563051211019004 2021

  23. [23]

    Milena Pustet, Elisabeth Steffen, and Helena Mihaljevic. 2024. Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models. InProceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024), Yi-Ling Chung, Zeerak Talat, Debora Nozza, Flor Miriam Plaza-del Arco, Paul Röttger, Aida Mostafazadeh Davani, ...

    work page doi:10.18653/v1/2024.woah-1.2 2024

  24. [24]

    Cesare Rollo, Gianmarco De Francisci Morales, Corrado Monti, and André Panisson. 2022. Communities, gateways, and bridges: Measuring attention flow in the reddit political sphere. InInternational Conference on Social Informatics. Springer, 3–19

    work page 2022

  25. [25]

    Giuseppe Russo, Luca Verginer, Manoel Horta Ribeiro, and Giona Casiraghi. 2023. Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning.Proceedings of the International AAAI Conference on Web and Social Media17 (June 2023), 742–753. doi:10.1609/icwsm.v17i1.22184

    work page doi:10.1609/icwsm.v17i1.22184 2023

  26. [26]

    Mattia Samory and Tanushree Mitra. 2018. Conspiracies Online: User Discussions in a Conspiracy Community Following Dramatic Events. Proceedings of the International AAAI Conference on Web and Social Media12, 1 (June 2018). doi:10.1609/icwsm.v12i1.15039

    work page doi:10.1609/icwsm.v12i1.15039 2018

  27. [27]

    Tim Schatto-Eckrodt, Lena Clever, and Lena Frischlich. 2024. The Seed of Doubt: Examining the Role of Alternative Social and News Media for the Birth of a Conspiracy Theory.Social Science Computer Review42, 5 (Oct. 2024), 1160–1180. doi:10.1177/08944393241246281 Publisher: SAGE Publications Inc

    work page doi:10.1177/08944393241246281 2024

  28. [28]

    Dominic Spohr. 2017. Fake news and ideological polarization: Filter bubbles and selective exposure on social media.Business information review34, 3 (2017), 150–160

    work page 2017

  29. [29]

    Amaury Trujillo and Stefano Cresci. 2022. Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald. Proc. ACM Hum.-Comput. Interact.6, CSCW2 (Nov. 2022), 526:1–526:28. doi:10.1145/3555639

    work page doi:10.1145/3555639 2022

  30. [30]

    Joseph E Uscinski, Karen Douglas, and Stephan Lewandowsky. 2017. Climate change conspiracy theories. InOxford research encyclopedia of climate science

    work page 2017

  31. [31]

    Tommaso Venturini. 2022. Online Conspiracy Theories, Digital Platforms and Secondary Orality: Toward a Sociology of Online Monsters.Theory, Culture & Society39, 5 (Sept. 2022), 61–80. doi:10.1177/02632764211070962 Publisher: SAGE Publications Ltd

    work page doi:10.1177/02632764211070962 2022

  32. [32]

    Jing Zeng, Mike S Schäfer, and Thaiane M Oliveira. 2022. Conspiracy theories in digital environments: Moving the research field forward.Convergence 28, 4 (Aug. 2022), 929–939. doi:10.1177/13548565221117474 Publisher: SAGE Publications Ltd. A Appendix As a robustness check for the SBERT-based semantic distance analysis, we also measure alignment using week...

    work page doi:10.1177/13548565221117474 2022