Pith. sign in

REVIEW 3 major objections 5 minor 2 cited by

Understanding Decentralized Social Feed Curation on Mastodon

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Mastodon users largely prefer chronological feeds for their transparency, but they accept rule-based algorithmic curation when the interface makes the algorithm legible and keeps control in the user's hands.

desk verdict A solid descriptive study of Mastodon feed curation whose causal claim about seamful design outruns its evidence. read the letter →

arxiv 2504.18817 v1 pith:WPOYWAC5 submitted 2025-04-26 cs.HC

Yuhan Liu , Emmy Song , Owen Xingjian Zhang , Jewel Merriman , Lei Zhang , Andrés Monroy-Hernández This is my paper · ORCID
classification cs.HC
keywords Mastodondecentralizedsocialmediafeedcurationseamfuldesignalgorithmictransparencyqualitativeuserstudychronologicalrule-based
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

This paper claims that Mastodon users' attachment to chronological feeds is not a blanket rejection of algorithms: they distrust opaque, engagement-driven machine learning, but they welcome rule-based curation when the interface makes the rules legible and keeps control in the user's hands. The evidence is a two-part interview study with 21 Mastodon users, paired with braids.social, a prototype that merges the home, local, and trending feeds into one semi-chronological feed controlled by priority sliders, source badges, and real-time feedback. Participants described the unified feed as more digestible than the default multi-feed layout and credited it with improving discoverability, while accepting algorithmic mixing mainly for catching up quickly and exploring during longer absences. If this holds, it grounds concrete design guidance for decentralized feed tools: keep chronological defaults, show where each post came from, and make priority settings adjustable with immediate visible effect.

What carries the argument

The load-bearing mechanism is braids.social, a web-based Mastodon client built on the platform's public APIs. It collapses the home, local, and trending feeds into a single semi-chronological stream: posts are fetched from each source in proportion to slider priorities (None, Low, Medium, High), merged within per-source chronological fragments, and labeled with a badge ('Users you follow', 'Hashtag you follow', 'Trending post', 'Local post', 'Prioritized account') showing where each post came from. A 'prioritized account' field lets users pin specific accounts above the mix. The design operationalizes seamful design, a design approach that makes an algorithm's workings visible and immediately adjustable, so that user acceptance of algorithmic curation can be observed and measured.

What would settle it

A representative study of a broader sample, for instance users of large general-purpose servers, non-English-speaking communities, and users who stick to the official app, comparing acceptance of rule-based curation with seams visible versus hidden: if a broader sample shows no increase in acceptance when the algorithm is made visible and adjustable, the paper's central claim would be refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that Mastodon users do not oppose algorithmic feeds as such; they oppose machine-learning feeds whose workings are hidden and whose purpose is engagement. Interviewed users prized the chronological feed because it is transparent and lets them see exactly what they missed, but they identified catching up after an absence and discovering new content as moments when algorithmic help would be welcome. When given braids.social, a unified feed in which home, local, and trending posts are mixed according to user-set priority sliders, with each post carrying a badge naming its source and the feed redrawing instantly on any change, nine of ten participants kept followed content at high priority while mixing in smaller amounts of local and trending posts. The authors conclude that seamful design, which makes the algorithm's seams visible, enhances people's acceptance of algorithmic feed curation, and that the unified feed makes information more digestible while adding discoverability. They also report that participants interpreted the slider levels high, medium, and low differently, so the legibility gains come with a need for finer granularity and stronger confirmation that the generated feed matches the settings.

Load-bearing premise

The load-bearing premise is that 21 self-selected users, recruited through the lead author's home server and interviewed over Zoom, are representative enough of Mastodon users at large to support generalizable design guidance.

Editorial extensions

If this is right

  • Feed curation tools for decentralized platforms should default to chronological order and make any algorithmic intervention visible, because transparency is what users value in the chronological feed.
  • Users' willingness to accept algorithmic curation tracks browsing intent: algorithmic help is wanted for quick catch-up after absence and for discovery, not for routine browsing.
  • Rule-based and machine-learning-based curation have distinct, irreplaceable roles, rule-based for configuring what kinds of posts to see and machine-learning for summarization and discovery, so future designs should co-design both rather than choose one.
  • A single unified feed with an adjustable mix of sources can reduce the under-utilization of the local and trending feeds and increase content discoverability.
  • Priority sliders need finer granularity and real-time confirmation, through badges and immediate redraws, to close the gap between users' mental models and the algorithm's actual behavior.

Reading between the lines

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

  • The legibility lesson likely transfers to centralized platforms: source badges and adjustable priority sliders could be tested on mainstream feeds, though the profit incentive there may undermine the trust that made them work on Mastodon.
  • The paper tested only rule-based curation; a natural extension is to give the same badge-and-slider treatment to a machine-learning ranking and measure whether acceptance rises as much, which would separate legibility from rule-basedness as the driver.
  • Because recruitment was skewed toward transparency-minded users, the observed acceptance of legible algorithmic curation may be a lower bound; users with weaker transparency concerns might accept even less legible curation or might find the slider interface too demanding.
  • The observed multi-client fragmentation suggests a testable design direction: a plugin or add-on ecosystem layered on existing Mastodon clients, rather than another stand-alone client, may better serve users who already juggle several apps.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper reports a two-part qualitative interview study with 21 Mastodon users. In Part 1, eleven participants were interviewed about how they perceive, interact with, and manage Mastodon feeds; the authors report a preference for chronological feeds and openness to algorithmic curation under conditions of transparency and user control. In Part 2, ten different participants evaluated braids.social, a web-based prototype that merges Mastodon feeds into a single semi-chronological feed with slider-based priority controls, source badges, and real-time redraw. The paper claims that seamful design enhances acceptance of algorithmic feed curation, discusses trade-offs between machine-learning-based and rule-based curation, and raises design implications for decentralized social media tools, including the choice between new apps and add-ons.

Significance. If the findings hold, the paper provides useful empirical grounding for an underexplored area: feed curation in decentralized social platforms. Strengths include the authenticity of participant quotes, the clear configuration table (Table 1), the transparent discussion of limitations in Section 8, and the inclusion of implementation pseudocode in Appendix B. The paper also makes a concrete design contribution with braids.social, which can serve as a reference point for future curation tools. However, the central claim about seamful design causally enhancing acceptance rests on a non-comparative study, and the Part 2 analysis lacks inter-rater reliability; these issues need to be addressed before the design implications can be considered robust.

major comments (3)
  1. [Section 6.2 and 7.1] The central claim that 'seamful design enhances people's acceptance of algorithmic feed curation' (Section 7.1) is a causal attribution based on a non-comparative usability study. The study has no baseline condition without seamful features (e.g., no badges, no real-time feedback, no explanation of the algorithm), and the ten Part 2 participants are a different group from the eleven Part 1 participants. Moreover, Part 1 participants explicitly referred to machine learning-based algorithms when discussing algorithmic feeds, while braids.social is rule-based; the authors themselves note this in Section 6.2. Consequently, the observed acceptance could be due to algorithm type, novelty, task demand, or courtesy bias, rather than to the seamful interface. Please reframe the conclusion as 'participants reported acceptance of a rule-based feed when interacting with a seamful interface' and, if the claim is to be retained, provide evidence from a within-subjects comparison that varies the seamfulness of the interface while holding the algorithm type constant.
  2. [Section 3.3] Part 1 coding used two coders with reported inter-rater reliability (Cohen's kappa ≥ 0.8), but Part 2 was analyzed by a single coder with no reliability check. This asymmetry undermines the dependability of the Part 2 themes used to support the paper's main design claims (e.g., 'badges were helpful', 'sliders were intuitive'). Please either add a second coder for a subset of Part 2 transcripts and report agreement, or explicitly add this as a limitation in Section 8.
  3. [Section 5.3 and Appendix B] The combinePosts() pseudocode in Appendix B randomly selects a non-empty category for each output post, rather than deterministically ordering by the priority levels. This random interleaving is not described in the design rationale of Section 5.1, which claims that 'we retained the chronological order within each feed's fragmentation.' The random interleaving may also explain P13's confusion in Section 6.3 about trending posts not appearing immediately at the top. Please clarify the stochastic nature of the algorithm, justify it as a design choice, and discuss whether it undermines the 'semi-chronological' description and the transparency the authors claim for the tool.
minor comments (5)
  1. [Section 3.3] The phrase 'one export coder' appears to be a typo; it should likely be 'one expert coder'.
  2. [Reference list] Reference [52] is a self-cited workshop report from the authors' own university blog; please ensure that it is cited appropriately or replaced with a peer-reviewed source if possible.
  3. [ACM Reference Format] The ACM Reference Format block lists '2018' as the publication year, but the manuscript is dated 2025; this should be corrected.
  4. [Section 2.2] In the comparison list, the claim that Mastodon's non-profit nature 'grants user greater agency in curating what they want to see in their feeds' is a broad assertion not directly supported by the data; consider softening it to 'may offer' or providing a citation.
  5. [Table 1] The column 'Accounts' in Table 1 is not clearly explained in the table caption; consider renaming it to 'Prioritized Accounts' to match the terminology used in Section 5.2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the qualitative derivation chain is self-contained and the sole self-citation is non-load-bearing.

full rationale

This paper is a two-part qualitative interview study with no fitted parameters, mathematical derivation, or predictive equation whose output could be equal to its input by construction. Part 1's thematic findings motivate the design of braids.social, and Part 2 evaluates that prototype with a disjoint group of participants; the reported conclusion that seamful design enhances acceptance of algorithmic curation is an interpretive generalization from interview data, not a numerical prediction entailed by the study design. The one self-citation, reference [52], appears in Section 2.1 as one of two citations for the factual statement that Mastodon adopts the ActivityPub Protocol; that fact is independently corroborated by references [53] and [80], so the self-citation is not load-bearing and does not constitute circularity. The attribution in Section 6.2 of the attitude difference to braids.social's transparency and user agency is a causal-inference claim whose non-comparative design is a validity concern, and the sampling limitations acknowledged in Section 8 are generalizability concerns; neither is a case where a result reduces to its own premises. No quoted reduction, renamed known result, or self-citation chain supporting the central claim was found.

Assumptions & free parameters 2 free parameters · 5 assumptions · 1 invented entities

All substantive claims rest on qualitative assumptions about self-report validity and platform behavior. No mathematical derivation occurs, so the ledger contains design parameters and domain assumptions rather than fitted constants. The only invented entity is the prototype itself, which lacks independent external evidence.

free parameters (2)
  • Slider priority weights = None=0, Low=1, Medium=2, High=3
    Chosen by hand without calibration; they determine the proportion of posts fetched from each feed in braids.social's getFeed() (Appendix B). The qualitative findings about slider ambiguity depend on this mapping, but it is not fitted or derived.
  • Posts per API request = 40
    Each Mastodon API request retrieves 40 posts; combined with slider weights this determines feed composition. It is a reasonable implementation choice, not fitted to data.
assumptions (5)
  • domain assumption Mastodon's feed structure: home, local, federated, and explore behave as described in Section 2.1, with home/local/federated chronological and explore ranked by trending score.
    The interview questions and prototype design assume this documented platform behavior. If it is wrong, the findings about feed-specific perceptions would be misattributed.
  • domain assumption Participants' verbal self-reports during interviews approximate their actual feed management behavior.
    Thematic analysis treats interview responses as evidence; Part 1 partially mitigates this with screen sharing, but Part 2 relies on self-report and think-aloud.
  • domain assumption Thematic analysis with the provided codebooks is a valid method for extracting themes from transcripts.
    The study adopts Braun and Clarke's thematic analysis approach; the validity of the findings depends on the acceptance of this method as appropriate for qualitative HCI research.
  • domain assumption Cohen's kappa >= 0.8 indicates adequate inter-rater agreement for Part 1 coding; Part 2 used a single coder, so this reliability check does not cover Part 2.
    The paper reports the threshold in Section 3.3 but does not provide per-code kappa values. The absence of reliability coding for Part 2 weakens but does not invalidate its thematic claims.
  • ad hoc to paper The linear weight mapping for sliders is a reasonable representation of None/Low/Medium/High priorities.
    The mapping is arbitrary and central to the prototype's behavior; no calibration or user test validated equal spacing as the right interpretation of the labels.
invented entities (1)
  • braids.social prototype
    purpose: Web-based Mastodon client that merges home, local, and trending feeds into one slider-configurable feed with source badges.
    No deployed URL, repository, or commit hash is given. Evidence of its usefulness comes only from the 10 participants in Part 2 of this study.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Understanding Decentralized Social Feed Curation on Mastodon." pith.science (2026). https://pith.science/paper/WPOYWAC5

@misc{pith2026250418817,
  author       = {Pith},
  title        = {Pith review of: Understanding Decentralized Social Feed Curation on Mastodon},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WPOYWAC5}},
  note         = {Machine review of arXiv:2504.18817}
}
read the original abstract

As centralized social media platforms face growing concerns, more users are seeking greater control over their social feeds and turning to decentralized alternatives such as Mastodon. The decentralized nature of Mastodon creates unique opportunities for customizing feeds, yet user perceptions and curation strategies on these platforms remain unknown. This paper presents findings from a two-part interview study with 21 Mastodon users, exploring how they perceive, interact with, and manage their current feeds, and how we can better empower users to personalize their feeds on Mastodon. We use the qualitative findings of the first part of the study to guide the creation of Braids, a web-based prototype for feed curation. Results from the second part of our study, using Braids, highlighted opportunities and challenges for future research, particularly in using seamful design to enhance people's acceptance of algorithmic curation and nuanced trade-offs between machine learning-based and rule-based curation algorithms. To optimize user experience, we also discuss the tension between creating new apps and building add-ons in the decentralized social media realm.

Figures

Figures reproduced from arXiv: 2504.18817 by the authors.

Figure 1
Figure 1. Advanced Web Interface in the Official Mastodon Web App. [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. An overview of how braids.social generate feed 5.1 Design Implications Informed by Part One Study From part one study, we identified two implications in designing feed curation tools in Mastodon: • Collapsing Posts into a Single Feed to Reduce Information Overload in Local and Federated Feeds: Participants from part one of the study reported under-utilization of the local and federated feeds due to the overwhelming … view at source ↗
Figure 3
Figure 3. Design Considerations of braids.social 5.2 Interface Design Recent work has validated the efficiency of slider-based design in enhancing transparency in recommendation systems and content moderation tools [38, 44, 77]. Inspired by this, we designed a user interface to collect users’ preference of how their unified feeds in braids.social are composed (See Figure 3b). Through sliders with values ranging from “None” to… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Seeing the Politics of Decentralized Social Media Protocols

    cs.SI 2025-05 conditional novelty 6.0 of 10

    A ten-component framework shows how four decentralized social media protocols allocate power over identity, curation, and infrastructure in different ways.

  2. Understanding Community-Level Blocklists in Decentralized Social Media

    cs.SI 2025-06 conditional novelty 5.0 of 10

    Across 1,807 Mastodon instances and 12 moderator interviews, community-level blocklists vary widely in purpose and transparency, and moderators balance openness, safety, and context when using them.

Reference graph

Works this paper leans on

84 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). https://doi.org/10.48550/arXiv.2303.08774

    arXiv doi:10.48550/arxiv.2303.08774 2023

  2. [2]

    Ishaku Hassan Anaobi, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Damilola Ibosiola, and Gareth Tyson. 2023. Will Admins Cope? Decentralized Moderation in the Fediverse. In Proceedings of the ACM Web Conference 2023 . 3109–3120. https://doi.org/10.1145/3543507.3583487

    arXiv 2023

  3. [3]

    Eytan Bakshy, Solomon Messing, and Lada A Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 6239 (2015), 1130–1132. https://doi.org/10.1126/science.aaa1160

    doi:10.1126/science.aaa1160 2015

  4. [4]

    Jack Bandy and Nicholas Diakopoulos. 2021. More accounts, fewer links: How algorithmic curation impacts media exposure in Twitter timelines. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–28. https://doi.org/10.1145/3449152

    work page doi:10.1145/3449152 2021

  5. [5]

    Shlomo Berkovsky and Jill Freyne. 2015. Personalized social network activity feeds for increased interaction and content contribution. Frontiers in Robotics and AI 2 (2015), 24. https://doi.org/10.3389/frobt.2015.00024 , Vol. 1, No. 1, Article . Publication date: April 2018. 18 Liu et al

    work page arXiv 2015

  6. [6]

    Michael Bernstein, S Kairam, B Suh, L Hong, and EH Chi. 2010. A torrent of tweets: managing information overload in online social streams. In Workshop on Microblogging: What and How Can We Learn From It (CHI EA ’10)

    2010

  7. [7]

    Michael S Bernstein, Bongwon Suh, Lichan Hong, Jilin Chen, Sanjay Kairam, and Ed H Chi. 2010. Eddi: interactive topic-based browsing of social status streams. In Proceedings of the 23nd annual ACM symposium on User interface software and technology. 303–312. https://doi.org/10.1145/1866029.1866077

    arXiv 2010

  8. [8]

    Bhumika Bhatt, Premal J Patel, and Hetal Gaudani. 2014. A review paper on machine learning based recommendation system. International journal of engineering development and research 2, 4 (2014), 3955–3961. https://rjwave.org/ijedr/ papers/IJEDR1404092.pdf

    2014

Show all 84 references
  1. [9]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/1478088706qp063oa

    2006 doi

  2. [10]

    Aggi Cantrill. 2022. Mastodon Struggles to Keep Up With Surge of New Users Fleeing Twitter. Bloomberg (7 November 2022). https://www.bloomberg.com/news/articles/2022-11-07/mastodon-struggles-to-keep-up-with-surge-of-new- users-fleeing-twitter#xj4y7vzkg

    2022

  3. [11]

    Lucio La Cava, Luca Maria Aiello, and Andrea Tagarelli. 2023. Drivers of social influence in the Twitter migration to Mastodon. Scientific Reports 13, 1 (2023), 21626. https://doi.org/10.1038/s41598-023-48200-7

    2023 doi

  4. [12]

    Yu-Shian Chiu, Kuei-Hong Lin, and Jia-Sin Chen. 2011. A social network-based serendipity recommender system. In 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) . IEEE, 1–5. https://doi.org/10.1109/ISPACS.2011.6146073

    2011

  5. [13]

    Wikipedia contributors. 2024. Fediverse — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Fediverse

    2024

  6. [14]

    Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. InProceedings of the 10th ACM conference on recommender systems . 191–198. https://doi.org/10.1145/2959100.2959190

    2016

  7. [15]

    Anwitaman Datta, Sonja Buchegger, Le-Hung Vu, Thorsten Strufe, and Krzysztof Rzadca. 2010. Decentralized online social networks. Handbook of social network technologies and applications (2010), 349–378. https://doi.org/10.1007/978- 1-4419-7142-5_17

    2010 doi

  8. [16]

    Algorithms ruin everything

    Michael A DeVito, Darren Gergle, and Jeremy Birnholtz. 2017. " Algorithms ruin everything" # RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI conference on human factors in computing systems . 3163–3174. https://do...

    2017

  9. [17]

    Nicholas Diakopoulos. 2014. Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism, Columbia University 10 (2014). https://doi.org/10.1080/21670811.2014.976411

    2014 arXiv

  10. [18]

    Kevin Duh, Tsutomu Hirao, Akisato Kimura, Katsuhiko Ishiguro, Tomoharu Iwata, and Ching-Man Au Yeung. 2012. Creating stories: Social curation of Twitter messages. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 6. 447–450. https://doi.org/10.1...

    2012 doi

  11. [19]

    Erwan Dujeancourt and Marcel Garz. 2023. The effects of algorithmic content selection on user engagement with news on twitter. The Information Society 39, 5 (2023), 263–281. https://doi.org/10.1080/01972243.2023.2230471

    2023

  12. [20]

    Upol Ehsan, Q Vera Liao, Samir Passi, Mark O Riedl, and Hal Daumé III. 2024. Seamful XAI: Operationalizing Seamful Design in Explainable AI. Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024), 1–29. https://doi.org/10.1145/3637396

    2024 doi

  13. [21]

    Elk. 2024. Elk: A Lightweight Mastodon Web Client. https://elk.zone/

    2024

  14. [22]

    Motahhare Eslami, Amirhossein Aleyasen, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. Feedvis: A path for exploring news feed curation algorithms. In Proceedings of the 18th acm conference companion on computer supported cooperative work & social computing . ...

    2015

  15. [23]

    Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I" like" it, then I hide it: Folk Theories of Social Feeds. In Proceedings of the 2016 cHI conference on human factors in computing systems . 23...

    2016

  16. [24]

    I always assumed that I wasn’t really that close to [her]

    Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. "I always assumed that I wasn’t really that close to [her]" Reasoning about Invisible Algorithms in News Feeds. In Proceedings of...

    2015

  17. [25]

    Farcaster. 2025. Farcaster Documentation. https://docs.farcaster.xyz/

    2025

  18. [26]

    FediDB. 2025. Fediverse Network Statistics. https://fedidb.org/

    2025

  19. [27]

    Fedilab. 2024. Fedilab: A Versatile Client for the Fediverse. https://fedilab.app/

    2024

  20. [28]

    Fedi.Tips. 2023. Which apps can I use? Should I use the official app or a third-party app? (2023). https://fedi.tips/which- apps-can-i-use-should-i-use-the-official-app-or-a-third-party-app/

    2023

  21. [29]

    KJ Feng, Xander Koo, Lawrence Tan, Amy Bruckman, David W McDonald, and Amy X Zhang. 2024. Mapping the Design Space of Teachable Social Media Feed Experiences. arXiv preprint arXiv:2401.14000 (2024). https://doi.org/10. 1145/3613904.3642120 , Vol. 1, No. 1, Article . Publicatio...

    2024 arXiv

  22. [30]

    Kevin Feng, David McDonald, and Amy Zhang. 2023. Teachable Agents for End-User Empowerment in Personalized Feed Curation

    2023

  23. [31]

    Jessica L Feuston, Alex S Taylor, and Anne Marie Piper. 2020. Conformity of eating disorders through content moderation. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (2020), 1–28. https://doi.org/10.1145/ 3392845

    2020

  24. [32]

    Richard Fletcher and Rasmus Kleis Nielsen. 2018. Automated serendipity: The effect of using search engines on news repertoire balance and diversity. Digital Journalism 6, 8 (2018), 976–989. https://doi.org/10.1080/21670811.2018.1502045

    2018 arXiv

  25. [33]

    Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems . 257–260. https://doi.org/10.1145/1864708.1864761

    2010

  26. [34]

    Amy Gesenhues. 2018. Facebook cuts off access to API platform for hundreds of thousands of inactive apps. (2018). https://martech.org/facebook-cuts-off-access-to-api-platform-for-hundreds-of-thousands-of-inactive-apps/

    2018

  27. [35]

    Homero Gil de Zúñiga, Zicheng Cheng, and Pablo González-González. 2022. Effects of the News Finds Me perception on algorithmic news attitudes and social media political homophily. Journal of Communication 72, 5 (2022), 578–591. https://doi.org/10.1093/joc/jqac025

    2022 doi

  28. [36]

    Eric Gilbert. 2012. Predicting tie strength in a new medium. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 1047–1056. https://doi.org/10.1145/2145204.2145360

    2012

  29. [37]

    Jay Graber. 2021. Ecosystem Review. https://gitlab.com/bluesky-community1/decentralized-ecosystem Archived at https://perma.cc/RJ2Y-H6YT

    2021

  30. [38]

    Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56 (2016), 9–27. https: //doi.org/10.1016/j.eswa.2016.02.013

    2016 doi

  31. [39]

    Wanrong He, Mitchell L Gordon, Lindsay Popowski, and Michael S Bernstein. 2023. Cura: Curation at Social Media Scale. Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (2023), 1–33. https://doi.org/10.1145/3610186

    2023 doi

  32. [40]

    Sohyeon Hwang, Priyanka Nanayakkara, and Yan Shvartzshnaider. 2023. Whose policy? Privacy challenges of decentralized platforms. In CHI’23 Workshops: Designing Technology and Policy Simultaneously: Towards A Research Agenda and New Practice . https://doi.org/10.2139/ssrn.4416746

    2023 doi

  33. [41]

    Ice Cubes. 2024. Ice Cubes: A Mastodon Client. https://github.com/Dimillian/IceCubesApp

    2024

  34. [42]

    Ivory. 2024. Ivory: A Mastodon Client by Tapbots. https://tapbots.com/ivory/

    2024

  35. [43]

    Ujun Jeong, Paras Sheth, Anique Tahir, Faisal Alatawi, H Russell Bernard, and Huan Liu. 2023. Exploring platform migration patterns between twitter and mastodon: A user behavior study. arXiv preprint arXiv:2305.09196 (2023). https://doi.org/10.1609/icwsm.v18i1.31348

    2023 arXiv

  36. [44]

    Shagun Jhaver, Alice Qian Zhang, Quan Ze Chen, Nikhila Natarajan, Ruotong Wang, and Amy X Zhang. 2023. Personalizing content moderation on social media: User perspectives on moderation choices, interface design, and labor. Proceedings of the ACM on Human-Computer Interaction 7...

    2023 doi

  37. [45]

    Denis Kotkov, Alan Medlar, and Dorota Glowacka. 2023. Rethinking serendipity in recommender systems. InProceedings of the 2023 Conference on Human Information Interaction and Retrieval. 383–387. https://doi.org/10.1145/3576840.3578310

    2023

  38. [46]

    Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. 2020. How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing 102 (2020), 393–411. https://doi.org/10.1007/s00607- 018-0687-5

    2020 doi

  39. [47]

    Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. 2016. A survey of serendipity in recommender systems. Knowledge-Based Systems 111 (2016), 180–192. https://doi.org/10.1016/j.knosys.2016.08.014

    2016 doi

  40. [48]

    Lucio La Cava, Sergio Greco, and Andrea Tagarelli. 2021. Understanding the growth of the Fediverse through the lens of Mastodon. Applied network science 6 (2021), 1–35. https://doi.org/10.1007/s41109-021-00392-5

    2021 doi

  41. [49]

    Lucio La Cava, Sergio Greco, and Andrea Tagarelli. 2022. Information consumption and boundary spanning in Decentralized Online Social Networks: The case of Mastodon users. Online Social Networks and Media 30 (2022), 100220. https://doi.org/10.1016/j.osnem.2022.100220

    2022

  42. [50]

    Kijung Lee and Mian Wang. 2023. Uses and Gratifications of Alternative Social Media: Why do people use Mastodon? arXiv preprint arXiv:2303.01285 (2023). https://doi.org/10.48550/arXiv.2303.01285

    2023 doi arXiv

  43. [51]

    Kristina Lerman and Rumi Ghosh. 2010. Information contagion: An empirical study of the spread of news on digg and twitter social networks. In Proceedings of the international AAAI conference on web and social media , Vol. 4. 90–97. https://doi.org/10.1609/icwsm.v4i1.14021

    2010 doi

  44. [52]

    Yuhan Liu, Varun Rao, Xingjian Zhang, Ryan Liu, Priyanka Nanayakkara, Zilin Ma, Kevin Feng, and Zhilin Zhang

  45. [53]

    Aymeric Mansoux and Roel Roscam Abbing. 2020. Seven theses on the fediverse and the becoming of FLOSS. (2020). , Vol. 1, No. 1, Article . Publication date: April 2018. 20 Liu et al

    2020

  46. [54]

    Mike Masnick. 2019. Protocols, Not Platforms: A Technological Approach to Free Speech. https://knightcolumbia.org/ content/protocols-not-platforms-a-technological-approach-to-free-speech

    2019

  47. [55]

    Mastodon gGmbH. [n. d.]. Mastodon - Social networking that’s not for sale. https://joinmastodon.org

  48. [56]

    Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019. Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI practice. Proceedings of the ACM on human-computer interaction 3, CSCW (2019), 1–23. https://doi.org/10.1145/3359174

    2019 doi

  49. [57]

    Eric Meyerson. 2012. Youtube now: Why we focus on watch time. YouTube Creator Blog 10 (2012)

    2012

  50. [58]

    Smitha Milli, Luca Belli, and Moritz Hardt. 2021. From optimizing engagement to measuring value. InProceedings of the 2021 ACM conference on fairness, accountability, and transparency . 714–722. https://doi.org/10.1145/3442188.3445933

    2021

  51. [59]

    Arvind Narayanan. 2023. Understanding Social Media Recommendation Algorithms. https://knightcolumbia.org/ content/understanding-social-media-recommendation-algorithms

    2023

  52. [60]

    Thao Ngo and Nicole Krämer. 2022. Exploring folk theories of algorithmic news curation for explainable design. Behaviour & Information Technology 41, 15 (2022), 3346–3359. https://doi.org/10.1080/0144929X.2021.1987522

    2022

  53. [61]

    Nicholson

    Matthew N. Nicholson. 2023. An Exploration of the Twitter to Mastodon Migration

    2023

  54. [62]

    Ivens Portugal, Paulo Alencar, and Donald Cowan. 2018. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications 97 (2018), 205–227. https://doi.org/10.1016/j.eswa.2017. 12.020

    2018 doi

  55. [63]

    Emilee Rader and Rebecca Gray. 2015. Understanding user beliefs about algorithmic curation in the Facebook news feed. In Proceedings of the 33rd annual ACM conference on human factors in computing systems . 173–182. https: //doi.org/10.1145/2702123.2702174

    2015

  56. [64]

    Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. 2019. Challenges in the decentralised web: The mastodon case. In Proceedings of the internet measurement conference . 217–229. https: //doi.org/10.1145/3355369.3355572

    2019

  57. [65]

    Urbano Reviglio. 2019. Serendipity as an emerging design principle of the infosphere: challenges and opportunities. Ethics and Information Technology 21, 2 (2019), 151–166. https://doi.org/10.1007/s10676-018-9496-y

    2019 doi

  58. [66]

    Michael Ridley. 2023. Using folk theories of recommender systems to inform human-centered explainable AI (HCXAI). The Canadian Journal of Information and Library Science46, 2 (2023), 1–19. https://doi.org/10.5206/cjils-rcsib.v46i2.15723

    2023 doi

  59. [67]

    Manuel Gomez Rodriguez, Krishna Gummadi, and Bernhard Schoelkopf. 2014. Quantifying information overload in social media and its impact on social contagions. In Proceedings of the international AAAI conference on web and social media, Vol. 8. 170–179. https://doi.org/10.1609/i...

    2014 doi

  60. [68]

    Clay Shirky. 2011. The political power of social media: Technology, the public sphere, and political change. Foreign affairs (2011), 28–41. http://www.jstor.org/stable/25800379

    2011

  61. [69]

    Chris Stokel-Walkerarchive. 2022. Twitter may have lost more than a million users since Elon Musk took over. MIT Technology Review (3 November 2022). https://www.technologyreview.com/2022/11/03/1062752/twitter-may-have- lost-more-than-a-million-users-since-elon-musk-took-over/

    2022

  62. [70]

    A Nicole Sump-Crethar. 2012. Making the most of Twitter. The reference librarian 53, 4 (2012), 349–354. https: //doi.org/10.1080/02763877.2012.704566

    2012

  63. [71]

    Aditya Surve, Aneesh Shamraj, and Swapneel Mehta. 2024. How Decentralization Affects User Agency on Social Platforms. arXiv preprint arXiv:2406.09035 (2024). https://doi.org/10.36190/2024.74

    2024 arXiv

  64. [72]

    The Bluesky Team. 2023. Algorithmic Choice with Custom Feeds. https://blueskyweb.xyz/blog/7-27-2023-custom- feeds

    2023

  65. [73]

    Tusky. 2024. Tusky for Mastodon. https://tusky.app/

    2024

  66. [74]

    Twitter. 2023. Twitter’s Recommendation Algorithm. https://blog.twitter.com/engineering/en_us/topics/open- source/2023/twitter-recommendation-algorithm

    2023

  67. [75]

    Kristen Vaccaro, Ziang Xiao, Kevin Hamilton, and Karrie Karahalios. 2021. Contestability for content moderation. Proceedings of the ACM on human-computer interaction 5, CSCW2 (2021), 1–28. https://doi.org/10.1145/3476059

    2021 doi

  68. [76]

    Christina Warren. 2012. Twitter’s API Update Cuts Off Oxygen to Third-Party Clients. (2012). https://mashable.com/ archive/twitter-api-big-changes

    2012

  69. [77]

    Tom Warren. 2021. Intel’s Bleep is a new AI-powered tool that filters out toxic speech in games. (April 2021). https://www.theverge.com/2021/4/8/22373290/intel-bleep-ai-powered-abuse-toxicity-gaming-filters The Verge

    2021

  70. [78]

    Dennis M Wilkinson. 2008. Strong regularities in online peer production. In Proceedings of the 9th ACM conference on Electronic commerce. 302–309. https://doi.org/10.1145/1386790.1386837

    2008

  71. [79]

    Julia Carrie Wong. 2018. Facebook overhauls News Feed in favor of’meaningful social interactions. The Guardian 12 (2018)

    2018

  72. [80]

    World Wide Web Consortium (W3C). 2018. ActivityPub: Distributed Social Networks Protocol . https://www.w3.org/TR/ activitypub/#Overview , Vol. 1, No. 1, Article . Publication date: April 2018. Understanding Decentralized Social Feed Curation on Mastodon 21

    2018

  73. [81]

    Zhilin Zhang, Jun Zhao, Ge Wang, Samantha-Kaye Johnston, George Chalhoub, Tala Ross, Diyi Liu, Claudine Tinsman, Rui Zhao, Max Van Kleek, et al. 2024. Trouble in Paradise? Understanding Mastodon Admin’s Motivations, Experiences, and Challenges Running Decentralised Social Medi...

    2024 doi

  74. [82]

    Reza Jafari Ziarani and Reza Ravanmehr. 2021. Serendipity in recommender systems: a systematic literature review. Journal of Computer Science and Technology 36 (2021), 375–396. https://doi.org/10.1007/s11390-020-0135-9

    2021 doi

  75. [83]

    mastodon

    Matteo Zignani, Sabrina Gaito, and Gian Paolo Rossi. 2018. Follow the “mastodon”: Structure and evolution of a decentralized online social network. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 12. 541–550. https://doi.org/10.1609/icwsm.v12...

    2018 doi

  76. [2024]

    https://freedom-to-tinker.com/ 2024/03/19/five-themes-discussed-at-princetons-workshop-on-decentralized-social-media/

    Five Themes Discussed at Princeton’s Workshop on Decentralized Social Media. https://freedom-to-tinker.com/ 2024/03/19/five-themes-discussed-at-princetons-workshop-on-decentralized-social-media/

    2024

Pith tools

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