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REVIEW 4 major objections 4 minor 108 references

Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote)

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A baby-food hashtag has become a gender filter on Xiaohongshu.

desk verdict A well-evidenced case study of hashtag re-appropriation as an audience-control tactic; the causal blocking claim should be softened to perceived effect. read the letter →

arxiv 2501.18210 v2 pith:MBLZHFPM submitted 2025-01-30 cs.HC cs.CYcs.IRcs.SI

Ruyuan Wan , Lingbo Tong , Tiffany Knearem , Toby Jia-Jun Li , Ting-Hao 'Kenneth' Huang , Qunfang Wu This is my paper · ORCID
classification cs.HCcs.CYcs.IRcs.SI
keywords hashtagre-appropriationrecommendationalgorithmsaudiencecontrolXiaohongshusafespacesonlinegender-basedharassmentfolktheoriesdigitalfeminism
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 argues that women on Xiaohongshu, a recommendation-driven social platform, have turned hashtags into audience filters. By attaching '#Baby Supplemental Food' to posts about makeup, pets, relationships, and daily life, they try to stop male users from being shown their content. The study combines analysis of 5,800 scraped posts with interviews of 24 users to trace how the practice began, spread, and changed. If the authors are right, users can reclaim partial control over algorithmic distribution through collective semantic misdirection, turning recommendation systems into a site of everyday resistance.

What carries the argument

The central object is the re-appropriated hashtag used as a semantic misdirection device. #BSF ('Baby Supplemental Food') is a topical label that the target audience, men, is presumed to find uninteresting, and the recommendation algorithm uses hashtags as a distribution signal, so the tag acts as an algorithmic audience filter rather than a content descriptor. The paper also organizes the practice on two axes, audience management (attract vs. block) and content relevance (relevant vs. irrelevant), yielding three hashtag uses: regular usage, traffic-driving, and guarding against male users. The feedback loop that carries the argument is that blocking success attracts more users, viral popularity brings traffic-driven co-optation, effectiveness drops, and women derive new hashtags such as #BSF(TC), #HG, and #MS to restart the loop.

What would settle it

A controlled field experiment would settle it: publish identical posts on Xiaohongshu with and without #BSF using fresh accounts, and compare the gender composition of viewers and engagement; if the tag does not shift the audience's gender composition, the audience-control effect is not real.

Watch

Extended reading notes

Core claim

The central claim is that the re-appropriation of #BSF is deliberate, communal, and effective-enough audience control, not random misuse or a simple meme. Of 3,311 posts using #BSF, 84.8% were unrelated to baby food and instead covered food, outfits, love, makeup, weight loss, parenting tips, packaging, and cats. Interviews with 24 users show that women learned the trick from peers, discussed it in private chat groups, and believed it reduced male viewership and harassment. The paper describes the process as 'blocking, attracting, and evolving': the hashtag originally blocked men, then attracted women seeking a safe space, then attracted advertisers and thirst traps, and finally lost power, leading women to create derived hashtags such as #BSF(TC), #Male Sterilization, and #185 Handsome Guy. The authors read this as everyday resistance within digital feminism and use it to argue that recommendation-driven platforms need 'dynamic audience control' features.

Load-bearing premise

The claim that #BSF actually blocks male users depends on the unverified assumption that Xiaohongshu's recommendation algorithm uses hashtag topic as a strong signal for who gets shown a post; the paper's support is folk theory plus two interviewees' impressions, and one interviewee reports the hashtag failing completely.

Editorial extensions

If this is right

  • Women can partially exclude unwanted male audiences from recommendation-driven feeds without platform support, by exploiting the algorithm's reliance on hashtag topic signals.
  • The tactic is self-limiting: as a blocking hashtag goes viral, traffic-driven users and men adopt it, and the blocking effect erodes, forcing women to invent derived hashtags.
  • Existing safety tools, including reporting, blocking, and privacy settings, do not meet creators' need to control who sees a post, which is why users turn to algorithmic workarounds.
  • Hashtag use can be described on two dimensions, audience management (attract vs. block) and content relevance (relevant vs. irrelevant), which distinguishes regular usage, traffic-driving, and guarding against male users.
  • The practice constitutes a form of everyday resistance that shifts some control over content distribution from the platform to users, even if users do not frame it as activism.

Reading between the lines

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

  • We infer that the same semantic-misdirection trick should work on other recommendation platforms: any group can pick a topic presumed boring to an out-group and use its hashtag as an audience filter, so analogous tags are likely to appear on TikTok, Instagram, and Douyin.
  • The paper's own failure case (P12) suggests the tactic is brittle: for content the algorithm is likely to push hard anyway, such as bikini photos, the hashtag signal can be overwhelmed, so effectiveness likely depends on post type and algorithm volatility.
  • A testable extension follows directly: a repeated scraping study could monitor hashtag-topic divergence over time and predict when a blocking hashtag is about to lose its power, before users abandon it.
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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

4 major / 4 minor

Summary. This paper investigates the re-appropriation of the #Baby Supplemental Food (#BSF) hashtag on the recommendation-driven platform Xiaohongshu, where women users deliberately attach a baby-food hashtag to posts unrelated to baby food in the hope of keeping male users out of their audiences. The authors combine a quantitative analysis of 5,800 scraped posts (hashtag–post relevance classification, BERTopic modeling, LIWC expression analysis, and hashtag co-occurrence networks) with 24 semi-structured interviews of Xiaohongshu users. They report that 84.8% of #BSF posts are unrelated to baby food, describe the main topics within those posts, document the emergence of derived hashtags (#BSF(TC), #Male Sterilization, #185 Handsome Guy), and identify motivations including platform strategy shifts, gender-based harassment, and ineffective reporting/blocking tools. The paper frames the practice as a form of everyday digital feminist resistance and proposes a design concept called 'dynamic audience control.'

Significance. If the central claim is accepted, the paper makes a useful contribution to HCI/CSCW scholarship on algorithmic folk theories, feminist HCI, and user self-governance on recommendation-driven platforms. Its strengths include a genuinely mixed-methods design, a transparent positionality statement, a detailed codebook and interview protocol in the appendices, and a rich set of interview excerpts that capture disagreement and nuance within the user community. The documentation of hashtag re-appropriation as a communal, evolving tactic, including the negative case reported by P12, is valuable empirical material. However, the paper's headline interpretive claim—that #BSF actually blocks male audiences—is not established by the data presented; the quantitative evidence shows hashtag–content mismatch, not audience composition, and the interview evidence is mixed. The contribution is therefore better described as documenting an intended and believed audience-management tactic with uncertain effectiveness, rather than demonstrating a working method of audience control.

major comments (4)
  1. The inference from the 84.8% irrelevance rate to 'demonstrating its effect in blocking male users' is not supported by the data. Hashtag–post relevance is a property of post text, while the blocking claim is a claim about the recommender's audience distribution. The quantitative analysis establishes that #BSF posts are mostly not about baby food, but it does not establish who saw those posts or that male viewership was reduced. The interview support for effectiveness rests on two self-reports (P05, P08), while P12's account in Section 5.1.2 and Section 5.2.2 reports a #BSF post receiving more than 20,000 views primarily from men, which directly contradicts a reliable blocking effect. Please either present outcome data on audience composition or algorithmic exposure, or reframe the claim as 'users intend and believe this tactic blocks male audiences, with mixed and largely unverified effectiveness.'
  2. The headline quantitative result rests on a GPT-4o-mini classifier validated on 100 manually annotated post titles, with an overall accuracy of 94% on a test set containing 32 relevant and 68 irrelevant posts. No confidence interval, per-class precision/recall, or agreement statistics for the final classification are reported. Since the 84.8% irrelevance estimate is load-bearing for the entire quantitative narrative, the paper should report a binomial confidence interval for that prevalence, class-level performance metrics, and ideally a larger or randomly sampled validation set. Without this, readers cannot assess how much of the 84.8% figure could be classifier noise or bias.
  3. The authors acknowledge in Section 6.5 that it is 'infeasible to determine if the data is biased or fully representative' for posts scraped from a recommendation-driven platform. Given this, the quantitative claims in Section 5.1.1 (e.g., 84.8% irrelevant, topic cluster sizes) should be explicitly presented as descriptive statistics for the collected sample rather than as population estimates. The current wording in the abstract and findings, which presents these figures without such hedging, overstates their scope.
  4. The paper's own evidence shows that the blocking effect was contested and degraded over time: P12 reported a spectacular failure, P10 and P06 described reduced effectiveness, and Section 5.3.4 documents traffic-driven co-optation. Yet the abstract and conclusion state that the practice 'blocks male audiences' without qualification. The Discussion already contains the right nuance about hashtag adaptability and limitations, so the central claims in the abstract and findings should be aligned with that nuance.
minor comments (4)
  1. [Section 5.1.3 and Figure 8] The text says 'Full results of the post-expression analysis can be found in Appendix 8,' but the figure is numbered as Figure 8 in Appendix E; please correct the cross-reference. The caption of Figure 8 mentions an upward arrow (↑) indicating direction of difference, but no arrows appear in the figure itself; either add the arrows or remove the mention from the caption.
  2. [Table 4] Several topic keywords in Table 4 appear to be artifacts or unexplained tokens (e.g., '16592' in topic 19, '6025' in topic 20, and 'ahhhh' in topic 19). Please clean or explain these tokens, or note that they are raw c-TF-IDF outputs.
  3. [Section 4.3.1 / Table 2] The participant recruitment mix is described clearly, but the paper does not state whether the interviewees who reported using #BSF were users of the derived hashtags as well; adding a column or a sentence on which participants used which hashtags would strengthen the connection between the interview data and the quantitative hashtag analysis.
  4. [References] Reference [57] is a Chinese-language source with a translated title; please provide the original title or a note that it is in Chinese, for consistency with the other Chinese-language citations.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the quantitative and qualitative analyses are descriptive and independently grounded; the hashtag-blocking inference is weak but not definitionally forced.

full rationale

The paper's derivation chain is empirical rather than formal. The headline hashtag-relevance statistic (84.8% irrelevant posts, Section 5.1.1) is a descriptive classifier output, not a parameter fitted to produce the study's conclusions, and no equation-level reduction is present. The load-bearing inference that #BSF 'blocks male users' is supported by interview self-reports (P05, P08) and folk theory rather than by the relevance analysis; this is an evidentiary weakness, not circularity, because the inference is not equivalent to the inputs by construction. The paper also reports contrary evidence (P12's bikini post receiving 20,000+ mostly male views), further showing the claim is not definitionally forced. The only self-citation is [70] in related work on Chinese digital feminism, which is background and not load-bearing. The topic model, LIWC expression analysis, and co-occurrence network are descriptive patterns with external benchmarks (manual annotation, kappa = 0.977; baseline hashtags). Section 6.5 explicitly acknowledges sampling limitations. There is no fitted parameter renamed as a prediction, no uniqueness theorem imported from the authors, and no ansatz smuggled in via self-citation. Per the hard rules, unsupported inference belongs in correctness risk, not circularity; the circularity score is therefore 1.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no physical or formal entities. Its main conceptual contribution, 'dynamic audience control,' is a design proposal rather than a falsifiable entity. The ledger therefore captures the methodological and domain assumptions that the empirical claims rest on.

free parameters (3)
  • BERTopic hyperparameters (n_neighbors=7, n_components=5, min_dist=0.01, min_cluster_size=15) = n_neighbors=7, n_components=5, min_dist=0.01, min_cluster_size=15
    Chosen through grid search and manual checks (Appendix D). They affect the topic clusters and thus the qualitative topic descriptions.
  • Co-occurrence network edge weight threshold = not specified
    The authors adjusted the threshold to 'highlight the most frequent co-occurrences' (Section 4.2.5), which affects the visualized network structure.
  • GPT-4o-mini few-shot examples = 16 example titles for category A, none for category B
    The prompt's examples were chosen by the authors; classification performance varies with this selection, and the model was validated on only 100 posts.
assumptions (5)
  • domain assumption Hashtag relevance classification using post titles only is a valid proxy for post-topic relevance.
    The paper reports that adding content reduced accuracy from 94% to 75% (Section 4.2.2), yet titles can be misleading or ironic, which is common in re-appropriated posts.
  • domain assumption Users' self-reports about the causes of view counts (e.g., fewer men seeing posts) are accurate.
    No platform-side log data is used; P05 and P08 report fewer men but P12 reports the opposite, and the paper has no independent verification of audience composition.
  • domain assumption The Chinese LIWC dictionary categories map onto the constructs of interest (anger, male/female references, sexual content).
    Lexicon-based LIWC cannot capture context, sarcasm, or code-switching, which may bias the Tukey HSD comparisons.
  • domain assumption Scraped posts are a representative sample of #BSF usage on Xiaohongshu.
    The authors state in the limitations (Section 6.5) that it is infeasible to determine if the scraped data is biased or fully representative.
  • domain assumption The folk theory that men are not interested in baby-food content and that hashtag relevance drives algorithmic distribution is correct.
    The entire audience-blocking interpretation relies on this unverified causal chain: hashtag topic to algorithm to gender of viewers. The paper presents no algorithmic or demographic evidence for it.

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Cite this review

Pith. "Pith review of Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote)." pith.science (2026). https://pith.science/paper/MBLZHFPM

@misc{pith2026250118210,
  author       = {Pith},
  title        = {Pith review of: Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MBLZHFPM}},
  note         = {Machine review of arXiv:2501.18210}
}
read the original abstract

Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explores how women on Xiaohongshu (rednote), a recommendation-driven social platform, proactively re-appropriate hashtags (e.g., #Baby Supplemental Food) by using them in posts unrelated to their literal meaning. The hashtags were strategically chosen from topics that would be uninteresting to the male audience they wanted to block. Through a mixed-methods approach, we analyzed the practice of hashtag re-appropriation based on 5,800 collected posts and interviewed 24 active users from diverse backgrounds to uncover users' motivations and reactions towards the re-appropriation. This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures.

Figures

Figures reproduced from arXiv: 2501.18210 by the authors.

Figure 1
Figure 1. Xiaohongshu’s primary interfaces include: (1) the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The four steps in the process of quantitative data collection and analysis. 1) Hashtag-Post Relevance Analysis for #BSF [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Topic clusters among #BSF posts that were irrel [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Results of post expression analysis using the Tukey HSD test. Six datasets are compared: baseline, #BSF_f (#BSF posts [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Co-Occurrence Network of Derived Hashtags. Each node represents a hashtag, and each edge indicates that two [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Evolution of the BSF case on Xiaohongshu. Initially, women began re-appropriating #BSF to protect their posts from [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Two-dimensional coordinate illustrating hashtag affordance in terms of audience management (attract vs. block) and [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Full results of post-expression analysis using the Tukey HSD test. Each cell in the matrix indicates whether the [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.