TikTok Search Recommendations: Governance and Research Challenges

Jacob van de Kerkhof; Rebecca Scharlach; Robert Gorwa; Taylor Annabell; Thales Bertaglia

REVIEW 3 major objections 6 minor 1 cited by

TikTok Search Recommendations: Governance and Research Challenges

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read TikTok steers users toward prewritten searches while denying authorship.

desk verdict First paper to treat TikTok search recommendations as a governance object; the empirical base is thin and the 'neutral aggregator' framing overreads the documentation, but the research agenda stands. read the letter →

arxiv 2505.08385 v2 pith:YSOM5QFS submitted 2025-05-13 cs.IR cs.CY

Taylor Annabell , Robert Gorwa , Rebecca Scharlach , Jacob van de Kerkhof , Thales Bertaglia This is my paper · ORCID

classification cs.IRcs.CY

keywords searchrecommendationsTikTokplatformgovernancetransparencycontentmoderationrecommendersystemsDigitalServicesActsocial

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

TikTok now prompts users to search by overlaying preformulated query terms on videos and in comment sections. This position paper argues that the feature is a genuinely new form of recommendation: the platform decides what is searchable, inverts the open-ended nature of search, yet claims to be nothing more than a neutral aggregator of user comments and common searches. The paper maintains that this 'neutral aggregator' framing is a strategic opacity that shields TikTok from responsibility for contextually harmful suggestions, such as a transphobic query on a trans creator's videos, while leaving users and creators with no way to disable, contest, or fully report the recommendations. Because the same feature is now spreading to YouTube Shorts and Instagram, the paper argues that its transparency, moderation, and data-access gaps are a pressing governance problem rather than a niche product quirk. It contributes a preliminary qualitative analysis of Dutch influencers and four computational research challenges for studying the feature at scale.

What carries the argument

The central object is the search recommendation itself: a preformulated query phrase TikTok places on or under a video and at the top of the comments section, which a user can tap to launch a search. The device that carries the argument is what the paper treats as TikTok's 'selective opacity'—a public-facing explanation that recommendations are generated by AI from only two user-driven factors (comments on the video and searches made after watching), offered through the Creator Academy, combined with the near-total absence of technical or policy detail in user-facing documents. The paper contrasts this thin account with the interface's asymmetrical affordances: some recommendations can be reported, others cannot; reporting targets results rather than the query; and creators' only lever is comment filtering, which is acknowledged by TikTok spokespeople but missing from official documentation. This gap between the claimed neutral-aggregator mechanism and the observable, context-dependent behavior of recommendations is what the paper uses to motivate both its governance analysis and its research agenda.

What would settle it

A large-scale audit could test the paper's core premise: take videos with recommendations, block or delete all comments and suppress post-view searches on a controlled set, and see whether recommendations persist; if they continue unchanged, the claim that TikTok merely aggregates user comments and searches would be false, and if a predictive model built from comments can match recommendations with high accuracy while no contextually harmful queries appear, the paper's transparency and harm claims would be weakened.

Watch

Extended reading notes

Core claim

The paper's central claim is that TikTok search recommendations are a new recommendation product that preformulates user queries, and that TikTok uses its own documentation to present itself as a neutral conduit: suggestions are said to be generated purely from user comments and what other users search after watching a video. The authors argue this framing is misleading and strategically opaque because the actual indexing of video descriptions, comments, or transcripts is unknown, the feature cannot be modified or disabled by users, creators are not notified of the recommendations attached to their content, and reporting options vary by interface location. Using a qualitative sample of search recommendations on videos of the ten most-followed Dutch influencers, the paper shows that recommendations can be contextually problematic regardless of surface innocuousness, citing 'Nikkie as a boy' on a trans creator's videos and relationship-speculation prompts on videos that never mention those relationships. The paper concludes that the feature poses unresolved governance challenges under the EU Digital Services Act—transparency of recommendation factors, dark-pattern concerns, and limited reporting of objectionable content—and lays out a four-part computational research agenda to study generation, coordinated manipulation, contextual harm, and topic classification.

Load-bearing premise

The strongest load-bearing premise is that TikTok's official documentation—particularly the Creator Academy's statement that recommendations come only from comments and post-video searches—can be read as a truthful description of how the feature actually works; the empirical examples also rest on a small, Dutch-only, three-day sample of the ten most-followed Dutch influencers.

Editorial extensions

If this is right

If TikTok's own documentation is the only public account, the feature likely falls short of the Digital Services Act's recommender-transparency requirements under Article 27, the anti-manipulation standard under Article 25, and the notice-and-action obligations under Article 16.
Users and creators would have no reliable way to contest a harmful or wrong recommendation, since the feature cannot be disabled, creators are not notified of attached queries, and reporting routes differ by placement.
Coordinated commenting can steer recommendations onto unrelated videos, as the 'Bisan and Motaz NFL' case shows, making the feature a plausible vector for organized political or commercial influence.
The four research challenges—modelling recommendation generation from comments, detecting coordinated behavior, classifying contextual harm, and categorizing topics—define a concrete computational agenda that can proceed with scraped public data even though the Research API excludes recommendations.
Adoption of similar preformulated-query features by YouTube Shorts and Instagram would carry the same transparency and governance problems onto additional platforms.

Reading between the lines

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

A direct causal test of the 'neutral aggregator' story is possible: if comments and post-view searches were fully blocked or filtered on a test video and recommendations persisted, the Creator Academy explanation would be falsified; conversely, if recommendations vanish, the transparency objection narrows to moderation and reporting gaps.
Because coordinated comments can surface a query across unrelated videos, search recommendations may be a cheaper amplification channel than ads or follower manipulation, one that bypasses ad-disclosure rules and leaves no advertiser paper trail.
The contextual-harm examples imply that automated detection of harmful recommendations will need creator-identity-aware and discourse-aware classifiers rather than keyword filters, since 'Nikkie as a boy' is only harmful given who Nikkie is.
Cross-platform comparison with YouTube Shorts and Instagram is the most direct way to test whether the transparency gap is intrinsic to preformulated queries or a specific TikTok choice.

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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.

Desk Editor's Note

read the letter

Colleague,

Worth a look. This is the first thing I've seen that treats TikTok's search recommendations as a governance problem rather than just a product quirk. The authors are right that the feature is new, under-documented, and likely to spread—YouTube Shorts already has it, Instagram has signaled intent. They do a solid job laying out the transparency, moderation, and user-control gaps and connecting them to DSA provisions. The examples ("Nikkie as a boy" under a trans creator's video, coordinated Super Bowl comments reshaping a search term) are vivid and do real work in showing why context matters.

The soft spots are real but not fatal. The qualitative study is genuinely preliminary: 10 Dutch influencers, three consecutive days, no coding scheme, no dataset or collection protocol. For a position paper that's fine as illustration, and the authors don't oversell it—the checklist marks most empirical sections N/A. More concerning is the "neutral aggregator" charge. The Creator Academy quote says recommendations are generated using AI based on a "variety of factors," two of which are comments and searches. The paper turns that into "simply aggregates comments and common searches" and "positions itself as a neutral intermediary." That's an interpretive leap, and the stress-test note has it right: the documentation doesn't say "simply" and explicitly names AI. The good news is the governance argument doesn't depend on the leap. The stronger point—TikTok discloses almost nothing about the "variety of factors," users can't disable the feature, creators get no policy-level transparency—stands on its own with the same evidence.

The DSA analysis is asserted more than argued. Saying Article 27 "arguably" covers search recommendations is honest, but the paper doesn't dig into whether a preformulated query string counts as part of a recommender system's inputs, outputs, or neither. That's a section for the authors to strengthen, not a reason to reject.

Citation pattern looks fine; they use journalism and a few academic sources, and the self-citation to their own dataset paper is legitimate since the dataset is the base for the sample.

Bottom line: it's a good research agenda paper, modest in its claims, with one overreach you can fix by softening language. I'd send it to peer review. The reader's ACCEPT verdict is about right, with the neutrality framing as the main revision point.

Recommendation: engage with it; the agenda is useful and the feature deserves scrutiny.

Referee Report

3 major / 6 minor

Summary. This position paper analyzes TikTok's search recommendation feature, which presents preformulated search queries on or under videos and in comments sections. The authors argue that this feature raises governance challenges—limited transparency about generation and moderation, weak user control, and potential contextual harms—and that these challenges are not adequately addressed by TikTok's current documentation, interface design, or data access provisions. The paper grounds this argument in an interpretive reading of TikTok's Creator Academy and support pages, journalistic reports, and a small preliminary dataset collected from Dutch influencers' videos. It then discusses the European Digital Services Act as a relevant regulatory framework and proposes four computational research challenges: modeling how recommendations are generated from comments, detecting coordinated behavior, identifying context-dependent harms, and classifying recommendation topics. The paper is explicitly agenda-setting rather than empirical.

Significance. The paper addresses a timely and understudied feature that sits at the intersection of recommender-system governance, search engine studies, and platform accountability. Its strength is in identifying a concrete, evolving product and connecting it to both user-facing harms (misgendering, speculation, coordinated amplification) and regulatory obligations under the DSA. The authors are to be credited for grounding their argument in public documentation and news reports, for clearly separating the four research challenges, and for appropriately hedging most of their claims as open questions. If revised, the paper could serve as a useful intellectual agenda for platform governance and computational social science research. However, the current version's significance is weakened by an overreading of TikTok's documentation and by the thinness of the empirical component; the central governance argument survives after those issues are corrected.

major comments (3)

[Abstract and Section 2] The claim that TikTok “simply aggregates comments and common searches” and that comments and searches are “the only factors TikTok attributes to shaping recommendations” is not supported by the quoted Creator Academy text, which states that recommendations are “generated using AI based on a variety of factors,” two of which are user comments and searches. The quote explicitly leaves room for additional factors and algorithmic mediation. Because the “neutral intermediary” and “sidesteps responsibility” framing appears in the abstract, introduction, and conclusion, this is a load-bearing interpretive step. Please revise to distinguish what TikTok literally discloses from the authors' critical argument that the disclosed explanation is vague and downplays TikTok's editorial role; the latter is well supported and does not require the “only factors” reading.
[Section 2, dataset paragraph] The preliminary qualitative study is described too thinly for the role it plays. The authors report selecting the 10 most-followed Dutch influencers, collecting recommendations over three consecutive days, and obtaining 92 recommendations under videos and 167 in comments, followed by a “qualitative thematic analysis,” but they provide no coding scheme, no sampling procedure, no handling of duplicates, no intercoder reliability, and no discussion of ethical considerations for using public influencer data. The exact count (“18 out of 23 recommended queries”) is presented as empirical evidence. Since the paper is a position paper, this can be fixed by expanding the methods paragraph and clearly labeling the data as illustrative; in the current form, the empirical component carries more weight than its description supports.
[Section 3, DSA discussion] The legal analysis is asserted rather than developed. The paper moves from “arguably includes these search recommendations” to “TikTok is required to provide transparency on recommendation factors under DSA Article 27(1),” and similarly asserts that Article 25(1) dark-patterns concerns apply, without discussing the DSA's definitions of “recommender system” or “online platform,” or explaining whether search recommendations count as “information” for Article 16 purposes. The hedged phrasing (“arguably,” “raises a compliance question”) is welcome, but the abstract's “despite requirements under regulatory frameworks like the DSA” makes this a central claim. Please add a short doctrinal justification or explicitly frame the DSA discussion as open questions that require legal interpretation.

minor comments (6)

[Section 2, Community Guidelines quote] The quote contains “FYF,” which appears to be a typo for “FYP” (For You Page); please verify against the original source and correct if needed.
[Figures 1 and 2] The figures are referenced in the text but no captions or images are included in the provided version; the final submission should include the screenshots with dates and appropriate permissions or citations.
[Paper checklist, item 1(e)] The checklist response says limitations are “NA directly, as this is a position paper,” but the paper presents original empirical observations and should include a dedicated limitations and data-availability statement; this inconsistency should be corrected.
[Section 4, Challenge 2] The challenge title “Detecting when coordinated behaviour affects recommendations” is slightly inaccurate for the example, which describes coordinated comments causing a recommendation; consider “Detecting coordinated behaviour that drives recommendations.”
[Section 2, Adobe Express survey] The phrase “According to an Adobe Express survey” would be more precise as “According to a survey commissioned by Adobe Express” if that is the case, and the citation should indicate the survey's methodology or sample if available.
[Conclusion] The sentence “Namely, it is users who shape recommendations through comments and search queries” is ambiguous; please clarify that this is TikTok's claimed framing, not the authors' conclusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: a position paper whose claims rest on external documentation and a preliminary qualitative sample, with no derivation or fitted prediction to reduce.

full rationale

This is a position paper, not a derivation. It contains no equations, no fitted parameters, and no prediction that is equivalent to an input by construction. The central claims are that TikTok provides limited transparency about search recommendations and that these recommendations pose governance and computational research challenges. These claims are grounded in external sources: TikTok's own documentation (Creator Academy, Community Guidelines, support pages), news reports, and a small qualitative dataset of search recommendations collected from Dutch influencers' videos. The dataset is explicitly described as preliminary and used for illustration, not as the basis for a derived result. The one author self-citation (Gui et al. 2024) is used only to identify the ten most-followed Dutch influencers; it does not supply any premise that is then renamed as a conclusion. The interpretive statement that TikTok 'positions itself as a neutral intermediary' is an analytical reading of the documentation, not a circular reduction: even if that inference were overstated (as the skeptic notes), the paper's governance agenda about transparency, user control, and research access stands independently of it. No step in the paper reduces to its own input, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no fitted parameters and no invented entities. It relies on three domain assumptions: that TikTok's public documentation faithfully represents how recommendations work, that the cited DSA provisions apply to the feature, and that the small Dutch sample is illustrative for the categories of harm discussed.

assumptions (3)

domain assumption TikTok's public documentation, including Creator Academy and Community Guidelines, accurately describes how search recommendations are generated and moderated.
The governance analysis in Section 2 relies on these documents as the authoritative description of the feature; if the documents are inaccurate or performative, the claim that TikTok positions itself as a neutral aggregator is weakened.
domain assumption The Digital Services Act transparency and reporting obligations (Articles 25, 27, 16) apply to search recommendations as recommender systems or as information.
The DSA compliance argument in Section 2 is asserted without doctrinal analysis; the application to search recommendations is plausible but not established.
domain assumption The small qualitative sample of Dutch influencers' videos from January to July 2024 is sufficient to illustrate the categories of harm discussed.
The paper uses example frequencies and illustrative cases to support governance challenges while explicitly disclaiming generalizability, so this assumption is load-bearing only for the illustrative force of the examples.

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Cite this review

Pith. "Pith review of TikTok Search Recommendations: Governance and Research Challenges." pith.science (2026). https://pith.science/paper/YSOM5QFS

@misc{pith2026250508385,
  author       = {Pith},
  title        = {Pith review of: TikTok Search Recommendations: Governance and Research Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YSOM5QFS}},
  note         = {Machine review of arXiv:2505.08385}
}

read the original abstract

Like other social media, TikTok is embracing its use as a search engine, developing search products to steer users to produce searchable content and engage in content discovery. Their recently developed product search recommendations are preformulated search queries recommended to users on videos. However, TikTok provides limited transparency about how search recommendations are generated and moderated, despite requirements under regulatory frameworks like the European Union's Digital Services Act. By suggesting that the platform simply aggregates comments and common searches linked to videos, it sidesteps responsibility and issues that arise from contextually problematic recommendations, reigniting long-standing concerns about platform liability and moderation. This position paper addresses the novelty of search recommendations on TikTok by highlighting the challenges that this feature poses for platform governance and offering a computational research agenda, drawing on preliminary qualitative analysis. It sets out the need for transparency in platform documentation, data access and research to study search recommendations.

Figures

Figures reproduced from arXiv: 2505.08385 by the authors.

**Figure 2.** Search recommendation appearing on 17 July [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

WhichTok? Comparing Three TikTok Data Acquisition Tools
cs.SI 2026-08 conditional novelty 6.0 of 10

Three TikTok data collection tools retrieve largely non-overlapping datasets for identical queries, especially for hashtag and keyword searches.

Reference graph

Works this paper leans on

23 extracted references · 21 canonical work pages · cited by 1 Pith paper

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Adobe Express . 2024. Using TikTok as a Search Engine . https://www.adobe.com/express/learn/blog/using-tiktok-as-a-search-engine. Accessed on 2025-05-07

work page 2024
[4]

Biino, M.; Whateley, D.; and Bhattacharya, S. 2024. TikTok 's search recommendations are amplifying rumors and misinformation, frustrating creators and sending users down rabbit holes. https://www.businessinsider.com/creators-tiktok-ai-tool-shows-search-sensational-false-narratives-2024-1. Accessed on 2025-05-07

work page 2024
[5]

Crimmins, T. 2023. Is TikTok ’s suggested search function doing more harm than good? https://www.dailydot.com/irl/tiktok-suggested-search-harm/. Accessed on 2025-05-07

work page 2023
[6]

DiBenedetto, C. 2024. ' Did I get the blue comment?': How TikTok activists are leveraging an unexpected platform feature. https://mashable.com/article/tiktok-blue-comments-for-palestine-operation-watermelon. Accessed on 2025-05-07

work page 2024
[7]

Doctorow, C. 2023. The ‘ Enshittification ’ of TikTok . https://www.wired.com/story/tiktok-platforms-cory-doctorow/. Accessed on 2025-05-07

work page 2023
[8]

Duffy, B. 2017. ( Not ) Getting Paid to Do What You Love : Gender , Social Media , and Aspirational Work . New Haven, CT: Yale University Press. ISBN 978-0-300-22766-6

work page 2017

Show all 23 references

[9]

M.; and Chi, E

Evans, B. M.; and Chi, E. H. 2009. Towards a Model of Understanding Social Search . ArXiv:0908.0595 [cs]

2009 arXiv
[10]

Graham, R. 2023. Investigating Google ’s Search Engine : Ethics , Algorithms , and the Machines Built to Read Us . Bloomsbury Publishing. ISBN 978-1-350-32522-7. Google-Books-ID: NtiVEAAAQBAJ

2023
[11]

Gui, H.; Bertaglia, T.; Goanta, C.; de Vries, S.; and Spanakis, G. 2024. Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTok. In International Conference on Advances in Social Networks Analysis and Mining, 3--12. Springer

2024
[12]

Lorenz, T. 2024. TikTok search suggestions are manufacturing influencer drama. https://www.washingtonpost.com/technology/2024/02/08/tiktok-search-suggestions-inaccurate/. Accessed on 2025-05-07

2024
[13]

Noble, S. U. 2018. Algorithms of Oppression : How Search Engines Reinforce Racism . New York, NY: New York University Press. ISBN 978-1-4798-3364-1

2018
[14]

Olari, V. 2024. Rise of unknown Romanian presidential candidate preceded by Telegram and TikTok engagement spikes. https://dfrlab.org/2024/12/12/romania-candidate-telegram-tiktok/. Accessed on 2025-05-07

2024
[15]

Schroeder, A. 2023. Is TikTok ’s search bar creating more drama? https://www.dailydot.com/unclick/tiktok-search-bar-explained/. Accessed on 2025-05-07

2023
[16]

TikTok. 2024 a . Introducing Search Ads Campaign on TikTok . https://ads.tiktok.com/business/en-US/blog/introducing-search-ads-campaign. Accessed on 2025-05-07

2024
[17]

TikTok. 2024 b . TikTok Community Guidelines : Accounts and Features . https://www.tiktok.com/community-guidelines/en/accounts-features#3. Accessed on 2025-05-07

2024
[18]

TikTok. 2025 a . How TikTok recommends content. https://support.tiktok.com/en/using-tiktok/exploring-videos/how-tiktok-recommends-content. Accessed on 2025-05-07

2025
[19]

TikTok. 2025 b . Keyword Moderation & Appeals Process . https://ads.tiktok.com/help/article/keyword-moderation-and-appeals-process. Accessed on 2025-05-07

2025
[20]

TikTok. 2025 c . TikTok Creator Academy : Empowering Creators to Grow and Succeed on TikTok . https://www.tiktok.com/creator-academy/en/article/search. Accessed on 2025-05-07

2025
[21]

N.; Helmond, A.; Dieter, M.; and Weltevrede, E

van der Vlist, F. N.; Helmond, A.; Dieter, M.; and Weltevrede, E. 2024. Super-appification: Conglomeration in the global digital economy. New Media & Society, 14614448231223419

2024
[22]

Vidgen, B.; and Derczynski, L. 2020. Directions in abusive language training data, a systematic review: Garbage in, garbage out. Plos one, 15(12): e0243300

2020
[23]

Zappavigna, M. 2015. Searchable talk: the linguistic functions of hashtags. Social Semiotics, 25(3): 274--291

2015

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Adobe Express . 2024. Using TikTok as a Search Engine . https://www.adobe.com/express/learn/blog/using-tiktok-as-a-search-engine. Accessed on 2025-05-07

work page 2024

[4] [4]

Biino, M.; Whateley, D.; and Bhattacharya, S. 2024. TikTok 's search recommendations are amplifying rumors and misinformation, frustrating creators and sending users down rabbit holes. https://www.businessinsider.com/creators-tiktok-ai-tool-shows-search-sensational-false-narratives-2024-1. Accessed on 2025-05-07

work page 2024

[5] [5]

Crimmins, T. 2023. Is TikTok ’s suggested search function doing more harm than good? https://www.dailydot.com/irl/tiktok-suggested-search-harm/. Accessed on 2025-05-07

work page 2023

[6] [6]

DiBenedetto, C. 2024. ' Did I get the blue comment?': How TikTok activists are leveraging an unexpected platform feature. https://mashable.com/article/tiktok-blue-comments-for-palestine-operation-watermelon. Accessed on 2025-05-07

work page 2024

[7] [7]

Doctorow, C. 2023. The ‘ Enshittification ’ of TikTok . https://www.wired.com/story/tiktok-platforms-cory-doctorow/. Accessed on 2025-05-07

work page 2023

[8] [8]

Duffy, B. 2017. ( Not ) Getting Paid to Do What You Love : Gender , Social Media , and Aspirational Work . New Haven, CT: Yale University Press. ISBN 978-0-300-22766-6

work page 2017