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REVIEW 3 major objections 5 minor 109 references

Fanfiction in the Age of AI: Community Perspectives on Creativity, Authenticity and Adoption

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Fanfiction community members predominantly perceive AI-generated content as a threat to the human-centered, participatory values of their space, with veteran writers more resistant than newer ones.

desk verdict A useful and careful survey of fanfiction community attitudes toward GenAI, with a few data-reporting wrinkles that warrant minor revision rather than rejection. read the letter →

arxiv 2506.18706 v1 pith:AMHKUC7O submitted 2025-06-23 cs.HC

Roi Alfassi , Angelora Cooper , Zoe Mitchell , Mary Calabro , Orit Shaer , Osnat Mokryn This is my paper · ORCID
classification cs.HC
keywords generativeAIfanfictioncreativecommunitiesparticipatoryculturehuman-AIco-creationtransparencylaborsurveystudy
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 tries to establish that fanfiction readers and writers mostly see generative AI as a threat to the human-centered, participatory values of their community, even as a minority of early adopters and newer writers view it as a useful tool. Surveying 157 active community members, it finds large majorities who fear AI stories will flood platforms, overshadow human work, weaken social bonds, and undermine authenticity, and who demand transparent disclosure of AI involvement. The authors interpret these responses as evidence of an early resistance-and-adoption phase, in which more experienced writers resist AI more strongly than writers with one to five years of involvement. This matters because fanfiction communities are early, vocal testbeds for how participatory creative communities can integrate AI without losing the values that define them.

What carries the argument

The argument is carried by a graduated AI-involvement scale: the survey asked readers (Q27) and writers (Q41) to rate their willingness to engage with stories produced under eight levels of AI involvement, from grammar and spell-checking only to fully AI-generated drafts with minimal editing. This scale produces the paper's core gradient, showing that acceptance is high for low-level assistance (82.7% would read work that used AI only for spelling and grammar) and collapses for full generation (about 10% of writers supported AI producing an entire story). The same instrument anchors the statistical comparisons between experience cohorts using Mann-Whitney U and chi-square tests, revealing the consistent resistance gradient among veteran writers.

What would settle it

A larger, more representative, or multilingual survey that found a majority of fanfiction members welcoming AI-authored stories under transparent labeling would contradict the paper's central claim, as would behavioral evidence that AI-labeled stories receive equal or greater readership on major fanfiction platforms.

Watch

Extended reading notes

Core claim

The central claim is that the fanfiction community currently perceives GenAI-authored content predominantly as a threat to its participatory, human-centered culture rather than as a neutral enhancement. The paper grounds this in survey figures: 76.4% (120/156) agreed that AI-authored fanfiction endangers the community's social aspects, 83.4% (131/157) worried that an influx of AI-created stories would overshadow human-authored ones, and 72.2% (112/155) reported negative feelings upon discovering that a story they read was AI-written. At the same time, 86% (135/157) insisted that authors be transparent about AI involvement, and 57.7% (90/156) said they had never knowingly read an AI-generated story even though most were unsure they could detect one. Writers were already using AI tools (74.4% reported using at least one), yet acceptance dropped sharply as AI involvement rose, and writers with over ten years of experience were significantly more resistant than newer writers at every level of AI involvement.

Load-bearing premise

The reported percentages stand or fall on whether the 157 self-selected, mostly North American, English-speaking respondents represent the broader fanfiction community, and on whether the bot filter removed only automated responses rather than real participants.

Editorial extensions

If this is right

  • Platform designers can address the transparency demand with user-driven, fine-grained self-disclosure of AI involvement, since 86% of participants want disclosure and acceptance varies sharply by level of AI involvement.
  • Opt-in and opt-out mechanisms for allowing fanfiction to be used in AI training would respond to the ethical concerns and the sense of theft that participants reported.
  • Recommendation systems that label and filter by AI involvement could preserve the visibility of human-authored stories while using AI to revive dormant fandoms.
  • If the experience gradient persists, community turnover will likely raise baseline acceptance of AI assistance over time, even if veteran resistance remains strong.
  • The gap between participants' low confidence in detecting AI and their strong quality objections suggests that disclosure, not content inspection, is the main lever on acceptance.

Reading between the lines

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

  • Because the survey cannot establish causation, a natural extension is a randomized experiment in which identical stories are labeled human-written or AI-assisted; if rejection tracks the label rather than the text, then threat perception is driven by provenance disclosure rather than by content quality.
  • The experience gradient implies a generational shift: if veteran members disproportionately leave or lose influence, community norms could move toward accepting AI assistance faster than the current aggregate resistance suggests.
  • The same participatory-value concerns likely apply to other non-commercial creative communities, such as collaborative writing circles, zine scenes, and amateur art spaces, but the paper does not test that generalization.
  • The small group of highly AI-familiar, heavy-usage writers may act as the diffusion bridge to wider adoption, but with only about one in ten writers in that group, their long-term influence remains uncertain.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper presents a survey-based study of 157 self-identified fanfiction community members, examining their attitudes toward generative AI (GenAI) in fanfiction writing and community dynamics. The authors recruited participants through fanfiction platforms, cleaned the data by removing 332 of 489 initial respondents whom they classified as bots, and analyzed the remaining 157 responses using descriptive statistics, group comparisons, and qualitative coding of open-ended questions. The central findings are that a majority of respondents perceive AI-authored fanfiction as a threat to the social and human-centered values of the community (e.g., 76.4% agreed that AI-authored fanfiction endangers social aspects, Q48_5; 83.4% worried about AI stories overshadowing human ones, Q48_6), and that more experienced writers are more resistant to AI integration than newer writers. The paper also proposes design interventions for fanfiction platforms, including AI-use disclosure, consent mechanisms for AI training, and ways to enrich community engagement. The manuscript is framed as contributing to HCI research on participatory creative communities and human-AI co-creation.

Significance. If the findings are valid, the paper makes a useful empirical contribution to the growing literature on how creative communities respond to generative AI. The survey covers both readers and writers, includes a substantial qualitative component with reported inter-coder reliability (86.6%, 99.3%, and 86.3% for Q29, Q36, and Q46), and connects the results to participatory culture and data-ethics frameworks. The design implications for AI-use labeling, consent, and community engagement are concrete and actionable. The experience-based differences between newer and veteran writers, if replicable, would be a valuable finding for platform designers. The paper also provides a demographic comparison against AO3 census data, which helps contextualize the sample, and it explicitly acknowledges the sample's skew toward North American and English-speaking respondents.

major comments (3)
  1. [Section 3.2.2] The bot-removal rule is load-bearing for the paper's central quantitative claims but is not validated. The authors removed 332 of 489 respondents (68%) based on a geographic cluster (221 respondents) and a behavioral heuristic (111 respondents who answered only the minimal 44 required questions in 5–6 seconds, provided no open-ended responses, and used email servers that 'do not require authentication'). The paper does not report any validation of this rule against a gold standard, such as manual inspection or a pilot with known human respondents. A fast human reader can plausibly answer Likert items in 5–6 seconds each and skip open-ended questions, and a group of legitimate respondents using shared Wi-Fi or attending an event could match the geographic pattern. If even a modest fraction of the 111 excluded respondents were human, the reported percentages (e.g., Q48_5, 76.4%, 120/156; Q48_6, 83.4%, 131/157; Q21, 72.2%, 112/155) could shift materially, and the excluded group may hold systematically different attitudes. Given that the abstract and conclusions generalize to 'the fanfiction community,' the authors should either validate the exclusion rule, provide a sensitivity analysis showing the robustness of headline percentages under different exclusion assumptions, or substantially soften the generalizations. This issue is distinct from the acknowledged sample skew and is not addressed in the Limitations section.
  2. [Section 5.1.2 and Section 4.2.3] Several reported denominators are inconsistent with the stated sample size and with each other, which undermines confidence in the quantitative results. In Section 5.1.2, the text describes responses to Q42 and Q43 using denominators of 72 (e.g., '36%, /72', '65% (47/72)'), while the paper states there are 90 writers in the final sample; no explanation is given for the missing 18 respondents. In Section 4.2.3, the figure '64.74%, 101/159' for Q15 exceeds the total sample of 157 respondents, which is arithmetically impossible; the correct denominator may be 156, but 101/156 is also inconsistent with the stated N. Similarly, Section 4.2.3 reports 'The writers among our participants (90/156)' although the total sample is 157 and the paper states there are 90 writers and 67 exclusive readers (summing to 157). These inconsistencies affect the credibility of the descriptive statistics. The authors should reconcile all reported denominators against the actual number of valid responses per question, and state the number of missing responses for each key item.
  3. [Section 5.2.2] There is an internal inconsistency in the reporting of ethical concerns that makes the prevalence unclear. The text states '68.6% (107/156) of participants (Q51)' and then reports 'A few participants, 6.4% (10/156), also indicated concerns about data privacy and AI environmental impact, with 7.6% (7/91) of the responses expressing such concern.' The relationship between the 107 participants and the subcategories is not specified, and the denominator 91 for the 7.6% is unexplained. More importantly, the sentence immediately before this (about 'potential infringement and originality') does not give a numeric prevalence, so the reader cannot tell whether the '68.6%' refers to any ethical concern or to a specific subcategory. Please clarify the coding and the exact basis for each percentage.
minor comments (5)
  1. [Abstract and Keywords] The keyword line contains a typo: 'Storytelling, , GenAI, Participatory community' has a double comma and the list is oddly phrased. Please revise to a clean comma-separated list.
  2. [Section 5.1.2] The sentence 'Most writers the use of AI for editing acceptable (36%, /72)' is grammatically incomplete and displays a missing number before the comma. It should read, for example, 'Most writers found the use of AI for editing acceptable (36%, 26/72).'
  3. [Section 5.1.3] The authors report multiple Mann-Whitney U tests comparing experience groups across five Likert items (Q41_1 through Q41_5) without any correction for multiple comparisons. Given the number of tests, some significant p-values near 0.04 may not survive adjustment. Please state whether corrections were considered and, if not, acknowledge this as a limitation.
  4. [Section 6.2.1] The discussion of the inconsistency between participants' uncertainty about identifying AI-generated text (Q49) and their reported confidence in having never read AI-generated fic (Q19) is interesting, but it would benefit from a more explicit reference to the possibility of undetected AI content, which the authors only gesture at.
  5. [References] Reference [3] is listed as 'Anonymous' with no author name; this is a valid citation for a pseudonymous AO3 census, but the entry should include a note clarifying the pseudonymous nature of the source (similar to how the 2013 AO3 Census is cited via 'centreforthelights').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical survey whose claims are directly supported by participant responses, and its self-citations are not load-bearing.

full rationale

This paper does not present a derivation chain, a fitted model, or a prediction from first principles. Its central claims are descriptive empirical findings from a survey: percentages such as 76.4% agreeing that AI-authored fanfiction endangers social aspects (Q48_5) or 83.4% concerned about AI stories overshadowing human ones (Q48_6) are direct tabulations of participant responses, not outputs of a model that was fitted to those same responses. There is no equation in which an input quantity is renamed as a result, and no parameter is fitted on a subset of data and then 'predicted' on a closely related quantity. The self-citations present in the paper (e.g., Shaer et al. [80] for brainwriting-style collaboration ideas, Mokryn and Ben-Shoshan [66] for creative metrics, Schmitt et al. [78] for identity-based interpretation, and Shaer et al. [81] for the survey design approach) are used as supporting literature or as interpretive lenses for design suggestions; none of them supplies a load-bearing premise that the survey findings depend on. The bot-removal procedure (Section 3.2.2) and the acknowledged sample skew toward North American and English-speaking participants (Section 6.6) are external-validity and data-quality concerns, not circularity: they do not make the reported perceptions equivalent to the paper's inputs by construction. The paper is self-contained as an empirical study, and its limitations are explicitly acknowledged in the text. Accordingly, the appropriate circularity score is 0.

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

The central claims depend on survey methodology assumptions, not on mathematical axioms or invented entities. No free parameters were fitted. The key risk is sample representativeness and bot filtering.

assumptions (3)
  • domain assumption Survey respondents answer honestly and their self-reported attitudes reflect their actual views.
    All Likert and open-ended results are interpreted as genuine community perceptions, Section 3.
  • domain assumption Bot detection correctly distinguishes human respondents from automated ones.
    332 respondents were removed based on geo-location, response time, and email heuristics, Section 3.2.2; misclassification would bias percentages.
  • domain assumption The self-selected sample is representative enough of the fanfiction community for the reported percentages to be meaningful.
    Recruitment via email lists, Dreamwidth, Discord, and social media, Section 3.2.1; the authors acknowledge skew toward North American and English-speaking participants.

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Cite this review

Pith. "Pith review of Fanfiction in the Age of AI: Community Perspectives on Creativity, Authenticity and Adoption." pith.science (2026). https://pith.science/paper/AMHKUC7O

@misc{pith2026250618706,
  author       = {Pith},
  title        = {Pith review of: Fanfiction in the Age of AI: Community Perspectives on Creativity, Authenticity and Adoption},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AMHKUC7O}},
  note         = {Machine review of arXiv:2506.18706}
}
read the original abstract

The integration of Generative AI (GenAI) into creative communities, like fanfiction, is reshaping how stories are created, shared, and valued. This study investigates the perceptions of 157 active fanfiction members, both readers and writers, regarding AI-generated content in fanfiction. Our research explores the impact of GenAI on community dynamics, examining how AI affects the participatory and collaborative nature of these spaces. The findings reveal responses ranging from cautious acceptance of AI's potential for creative enhancement to concerns about authenticity, ethical issues, and the erosion of human-centered values. Participants emphasized the importance of transparency and expressed worries about losing social connections. Our study highlights the need for thoughtful AI integration in creative platforms using design interventions that enable ethical practices, promote transparency, increase engagement and connection, and preserve the community's core values.

Figures

Figures reproduced from arXiv: 2506.18706 by the authors.

Figure 1
Figure 1. A fanfiction contributor shares a fanfiction story created by ChatGPT in August 2024 [100]. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Prominent fandoms (a) and platforms (b) used by our participants. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Participants’ willingness to read or engage with stories varies based on the level of AI involvement. A dark shade [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Writers’ views on incorporating AI into their creative process (Q41). The dark shade represents participants who [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Writers’ perceptions regarding (a) acceptable scenarios for collaborating with AI and (b) aspects of writing where AI [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Reference graph

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Pith tools

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