pith. sign in

arxiv: 2604.20119 · v1 · submitted 2026-04-22 · 💻 cs.HC

Zeitgeist-Aware Multimodal (ZAM) Datasets of Pro-Eating Disorder Short-Form Videos: An Idea Worth Researching

Eden Shaveet , Zefan Sramek , Yumi Hamamoto , Jing Du , Scott Griffiths , Thalia Zhang , Thalia Viranda , William Hornby
show 3 more authors
Flora Salim Koji Yatani Tanzeem Choudhury
This is my paper

Pith reviewed 2026-05-10 00:03 UTC · model grok-4.3

classification 💻 cs.HC
keywords pro-eating disordermultimodal datasetsshort-form videozeitgeist-awarecontent detectionsocial mediaeating disordersonline moderation
0
0 comments X

The pith

Pro-ED detection in short videos requires expert-annotated multimodal datasets that update with new memes and terminology.

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

Current methods for identifying pro-eating disorder content rely mostly on text and cannot keep up with the fast-changing references, memes, and visual cues in short-form videos. This leaves no solid expert-labeled standard for training detection models or studying the content in real time. The paper proposes zeitgeist-aware multimodal datasets, called ZAM, that are built and updated continuously so their rules for what counts as pro-ED shift as online culture changes. These collections would let researchers track how pro-ED ideas move through video, audio, and text together. If built, they could support better moderation tools and studies across fields interested in online health messaging.

Core claim

The paper claims that reliable identification of pro-eating disorder content is blocked by two limits: over-reliance on text signals that ignore multimodal video elements, and inability to track rapid shifts in terminology, memes, and context. It therefore proposes zeitgeist-aware multimodal (ZAM) datasets—continuously curated collections of annotated pro-ED short-form videos whose inclusion criteria evolve with the memetic zeitgeist—as the missing reference standard needed for real-time research and strong multimodal model training.

What carries the argument

ZAM datasets: continuously curated, expert-annotated multimodal collections of short-form video whose inclusion rules adapt as new cultural references enter the memetic zeitgeist.

If this is right

  • Multimodal detection models trained on ZAM data would capture visual and audio signals that text-only systems miss.
  • Researchers could conduct timely studies of how pro-ED sentiment spreads through short-form platforms.
  • Moderation systems could become more responsive to emerging content patterns.
  • Multiple fields studying online health communication would gain a shared, time-sensitive reference resource.

Where Pith is reading between the lines

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

  • The same evolving-criteria approach could apply to other fast-shifting harmful content such as misinformation or self-harm promotion.
  • Hybrid human-AI pipelines would likely be required for scale, introducing new questions about annotation consistency.
  • Platforms might need to supply anonymized video streams to make ongoing curation feasible.

Load-bearing premise

That continuously curated datasets with inclusion criteria that evolve alongside the memetic zeitgeist can be practically maintained at scale without excessive bias or resource demands.

What would settle it

A pilot curation effort that produces high rates of expert disagreement on borderline examples or that requires unsustainable annotation hours per new meme wave would show the ZAM approach cannot scale as proposed.

read the original abstract

Objective: Reliable identification of pro-eating disorder (pro-ED) content online suffers from two pervasive problems: 1) existing methods predominantly rely on text-based signals, failing to capture the inherently multimodal nature of multimedia content; and 2) these methods struggle to keep pace with the rapid evolution of references, memes, terminology, and contextual cues that underlie this content. Together, these limitations point to a gap: the absence of an expert-annotated reference standard capable of supporting real-time research and robust multimodal detection model training for pro-ED content on short-form video platforms. Method: To address this, we propose "zeitgeist-aware" multimodal (ZAM) datasets: continuously curated collections of annotated multimodal pro-ED content with inclusion criteria that evolve alongside the memetic zeitgeist: the variable essence of what is considered pro-ED as new media and references come into the cultural zeitgeist and are absorbed and interpreted in online spaces. Results: We present a rationale for such datasets, define their core characteristics, outline approaches for their curation, and describe our progress toward that end. Discussion: This dataset and pipeline architecture may benefit researchers across several fields who are interested in how pro-ED sentiment is encoded and transmitted through short-form video content across time, including for the purpose of responsive moderation efforts.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript proposes 'zeitgeist-aware multimodal (ZAM) datasets' to address gaps in pro-eating disorder (pro-ED) content detection on short-form video platforms. It argues that existing methods are limited by text-only signals and static references, and introduces continuously curated, expert-annotated multimodal collections whose inclusion criteria adapt to evolving memetic trends, terminology, and cultural references. The paper defines core dataset characteristics, sketches curation approaches, and reports initial progress toward implementation.

Significance. If operationalized, ZAM datasets could enable more accurate multimodal model training and longitudinal studies of how pro-ED sentiment is encoded and transmitted in short-form video, supporting both research and responsive moderation. The proposal correctly identifies the absence of adaptive, expert-annotated multimodal references as a barrier to progress in this domain.

major comments (2)
  1. [Method] Method section: The sketched curation approaches provide no concrete protocols for versioning inclusion criteria, maintaining inter-annotator agreement across successive zeitgeist windows, or bounding annotation workload under rapid memetic turnover; these mechanisms are load-bearing for the central claim that reliable, continuously updated references can be produced at scale.
  2. [Results] Results section: The description of 'progress toward that end' contains no pilot data, feasibility metrics, or validation experiments demonstrating that expert annotation remains reliable and resource-bounded when criteria evolve; without such evidence the proposal remains untested.
minor comments (1)
  1. [Abstract] The abstract and Discussion could more explicitly separate the high-level dataset definition from the operational curation pipeline to improve clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential value of zeitgeist-aware multimodal datasets in addressing limitations in pro-ED content detection. We address each major comment below, focusing on the manuscript's scope as a conceptual proposal.

read point-by-point responses

  1. Referee: [Method] Method section: The sketched curation approaches provide no concrete protocols for versioning inclusion criteria, maintaining inter-annotator agreement across successive zeitgeist windows, or bounding annotation workload under rapid memetic turnover; these mechanisms are load-bearing for the central claim that reliable, continuously updated references can be produced at scale.

    Authors: We agree that the Method section provides only high-level sketches of curation approaches rather than detailed, operational protocols. The manuscript is positioned as an idea paper that defines core characteristics and outlines general strategies, with the explicit goal of stimulating research rather than presenting a fully engineered system. We acknowledge that mechanisms for versioning inclusion criteria, sustaining inter-annotator agreement across evolving windows, and managing annotation workload are critical to the central claim. In the revised version we will expand this section to include preliminary frameworks for these elements, informed by our initial curation efforts, while clearly noting that full protocols remain future work. revision: yes

  2. Referee: [Results] Results section: The description of 'progress toward that end' contains no pilot data, feasibility metrics, or validation experiments demonstrating that expert annotation remains reliable and resource-bounded when criteria evolve; without such evidence the proposal remains untested.

    Authors: The Results section reports only on initial progress toward implementation, consistent with the manuscript's framing as a proposal rather than an empirical study. We recognize that the absence of pilot data, feasibility metrics, or validation experiments leaves the operational claims untested at present. The current work focuses on rationale and conceptual definition; empirical validation of annotation reliability under evolving criteria is planned as subsequent research. We will revise the manuscript to more explicitly separate the conceptual contributions from the status of ongoing implementation work. revision: partial

Circularity Check

0 steps flagged

No circularity: conceptual proposal without derivations or fitted quantities

full rationale

This manuscript proposes zeitgeist-aware multimodal (ZAM) datasets as a conceptual solution to gaps in pro-ED content detection. It defines high-level characteristics, sketches curation approaches, and presents a rationale, but contains no equations, derivations, predictions, or parameter fitting. No load-bearing steps reduce to self-referential inputs, self-citations, or renamed empirical patterns. The central claims rest on definitional and architectural descriptions that do not invoke uniqueness theorems or ansatzes from prior work by the same authors. As a non-quantitative idea paper, the derivation chain is empty and self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The proposal rests on the domain assumption that pro-ED content is multimodal and culturally evolving, with the ZAM dataset concept introduced as the primary new element without supporting evidence yet.

axioms (1)
  • domain assumption Pro-ED content on short-form video platforms is inherently multimodal and evolves rapidly with cultural references and memes
    Stated in the objective as the basis for needing zeitgeist-aware datasets.
invented entities (1)
  • ZAM datasets no independent evidence
    purpose: Continuously curated, expert-annotated multimodal collections of pro-ED content with evolving inclusion criteria
    New concept introduced to fill the identified gap in reference standards for real-time research and model training.

pith-pipeline@v0.9.0 · 5577 in / 1203 out tokens · 42651 ms · 2026-05-10T00:03:22.070261+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

  1. [1]

    Dina L. G. Borzekowski, Summer Schenk, Jenny L. Wilson, and Rebecka Peebles. e-Ana and e-Mia: A content analysis ofpro-eatingdisorderwebsites.American Journal of Public Health,100(8):1526–1534,August2010

    work page

  2. [2]

    The urgent matter of online pro-eating disorder content and children: Clinical practice.European Journal of Pediatrics,174(4):429–433,January2015

    Kathleen Custers. The urgent matter of online pro-eating disorder content and children: Clinical practice.European Journal of Pediatrics,174(4):429–433,January2015

    work page

  3. [3]

    The ana sanctuary: Women’s pro-anorexia nar- ratives in cyberspace.Journal of International Women’s Studies,4(2):31–45,April2003

    Karen Dias. The ana sanctuary: Women’s pro-anorexia nar- ratives in cyberspace.Journal of International Women’s Studies,4(2):31–45,April2003

    work page

  4. [4]

    Lewis and Alexis E

    Stephen P. Lewis and Alexis E. Arbuthnott. Searching for thinspiration:Thenatureofinternetsearchesforpro-eating disorder websites.Cyberpsychology, Behavior, and Social Networking,15(4):200–204,April2012

    work page

  5. [5]

    CodieR.RouleauandKristinM.vonRanson.Potentialrisks of pro-eating disorder websites.Clinical Psychology Review, 31(4):525–531,June2011

    work page

  6. [6]

    Pro-eating disorder web- sites: Users’ opinions.European Eating Disorders Review, 15(3):196–206,March2007

    Emese Csipke and Outi Horne. Pro-eating disorder web- sites: Users’ opinions.European Eating Disorders Review, 15(3):196–206,March2007

    work page

  7. [7]

    Hidden in plain sight: Explor- ingtheintersectionsofmentalhealth,eatingdisorders,and content moderation on TikTok

    Charles Bickham, Kia Kazemi-Nia, Luca Luceri, Kristina Lerman, and Emilio Ferrara. Hidden in plain sight: Explor- ingtheintersectionsofmentalhealth,eatingdisorders,and content moderation on TikTok. InWorkshop Proceedings of the 18th International AAAI Conference on Web and Social Media,Rochester,NY,January2024.SocialScienceResearch Network

    work page

  8. [8]

    #thyghgapp: Insta- gramcontentmoderationandlexicalvariationinpro-eating disorder communities

    Stevie Chancellor, Jessica Annette Pater, Trustin Clear, Eric Gilbert, and Munmun De Choudhury. #thyghgapp: Insta- gramcontentmoderationandlexicalvariationinpro-eating disorder communities. InProceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & So- cial Computing,CSCW’16,page1201–1213.ACM,February 2016

    work page 2016

  9. [9]

    Differences in body image preoccupation following online exposure to fitspiration and thinspiration images.Journalism and Media,7(1):40,February2026

    Tindara Caprì. Differences in body image preoccupation following online exposure to fitspiration and thinspiration images.Journalism and Media,7(1):40,February2026

    work page

  10. [10]

    Zhi Wei Yee, Scott Griffiths, Matthew Fuller-Tyszkiewicz, Khandis Blake, Ben Richardson, and Isabel Krug. The differential impact of viewing fitspiration and thinspira- tion images on men’s body image concerns: An experimen- tal ecological momentary assessment study.Body Image, 35:96–107,December2020

    work page

  11. [11]

    Pater, Mun- mun De Choudhury, and David A

    Stevie Chancellor, Yannis Kalantidis, Jessica A. Pater, Mun- mun De Choudhury, and David A. Shamma. Multimodal classification of moderated online pro-eating disorder con- tent. InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, page 3213–3226. ACM,May2017. 5of6

    work page 2017

  12. [12]

    EDTok: A dataset for eatingdisordercontentonTikTok,2025

    CharlesBickham,BryanRamirez-Gonzalez,MinhDucChu, Kristina Lerman, and Emilio Ferrara. EDTok: A dataset for eatingdisordercontentonTikTok,2025

    work page 2025

  13. [13]

    Big- TokDetect: A clinically-informed vision-language modeling framework for detecting pro-bigorexia videos on TikTok, 2025

    Minh Duc Chu, Kshitij Pawar, Zihao He, Roxanna Shar- ifi, Ross Sonnenblick, Magdalayna Curry, Laura D’Adamo, LindsayYoung, StuartB Murray,and KristinaLerman. Big- TokDetect: A clinically-informed vision-language modeling framework for detecting pro-bigorexia videos on TikTok, 2025

    work page 2025

  14. [14]

    Unraveling the multimodal features and audience responses to thinspira- tion videos: A computational analysis of Chinese TikTok (Douyin).Body Image,56:102053,March2026

    Yuzhou Tao, Shaojing Sun, and Jinbo He. Unraveling the multimodal features and audience responses to thinspira- tion videos: A computational analysis of Chinese TikTok (Douyin).Body Image,56:102053,March2026

    work page

  15. [15]

    Yahoo removes pro-eating disorder sites.The Boston Globe,August2001

    Catherine Holahan. Yahoo removes pro-eating disorder sites.The Boston Globe,August2001

    work page

  16. [16]

    Girls who don’t eat

    Oprah Winfrey. Girls who don’t eat. The Oprah Winfrey Show,October2001

    work page

  17. [17]

    Nurturing an anorexia obsession.The Los Angeles Times,February2002

    Lynell George. Nurturing an anorexia obsession.The Los Angeles Times,February2002

    work page

  18. [18]

    Follow-up: Tumblr’s new policy against pro-self-harm blogs, March 2012

    Tumblr Staff. Follow-up: Tumblr’s new policy against pro-self-harm blogs, March 2012. Retrieved from https://staff.tumblr.com/post/18563255291/ follow-up-tumblrs-new-policy-against

    work page arXiv 2012

  19. [19]

    Content moderation, AI, and the ques- tionofscale.Big Data & Society,7(2),July2020

    Tarleton Gillespie. Content moderation, AI, and the ques- tionofscale.Big Data & Society,7(2),July2020

    work page

  20. [20]

    Ghofrane Merhbene, Alexandre Puttick, and Mascha Kurpicz-Briki. Investigating machine learning and natural language processing techniques applied for detecting eat- ing disorders: A systematic literature review.Frontiers in Psychiatry,15,March2024

    work page

  21. [21]

    Harris, Grace Whitehead, Felicity Angelopoulos, Ben Stone, Wesley Grey, and Simon Dennis

    Scott Griffiths, Emily A. Harris, Grace Whitehead, Felicity Angelopoulos, Ben Stone, Wesley Grey, and Simon Dennis. DoesTikTokcontributetoeatingdisorders?Acomparisonof the TikTok algorithms belonging to individuals with eating disorders versus healthy controls.Body Image, 51:101807, December2024

    work page

  22. [22]

    ExploringproblematicTikTok useandmen- tal health issues: A systematic review of empirical studies

    Lakshit Jain, Luis Velez, Surya Karlapati, Mary Forand, Rajasekhar Kannali, Rao Ahmed Yousaf, Rizwan Ahmed, ZouinaSarfraz,PearlA.Sutter,ChristianAnthonyTallo,and SaeedAhmed. ExploringproblematicTikTok useandmen- tal health issues: A systematic review of empirical studies. Journal of Primary Care & Community Health, 16, March 2025

    work page 2025

  23. [23]

    Social drivers and al- gorithmic mechanisms on digital media.Perspectives on Psychological Science,19(5):735–748,July2023

    Hannah Metzler and David Garcia. Social drivers and al- gorithmic mechanisms on digital media.Perspectives on Psychological Science,19(5):735–748,July2023

    work page

  24. [24]

    You can (not) say what you want: Using algospeak to contest and evade algorithmic content moderation on TikTok.Social Media + Society,9(3),July2023

    Ella Steen, Kathryn Yurechko, and Daniel Klug. You can (not) say what you want: Using algospeak to contest and evade algorithmic content moderation on TikTok.Social Media + Society,9(3),July2023

    work page

  25. [25]

    Beyond the hashtag: Circumventing con- tent moderation on social media.New Media & Society, 20(12):4492–4511,May2018

    Ysabel Gerrard. Beyond the hashtag: Circumventing con- tent moderation on social media.New Media & Society, 20(12):4492–4511,May2018

    work page

  26. [26]

    Greene, Hannah N

    Amanda K. Greene, Hannah N. Norling, Lisa M. Brown- stone,ElanaK.Maloul,CaityRoe,andSarahMoody.Visions of recovery: A cross-diagnostic examination of eating disor- derpro-recoverycommunitiesonTikTok.Journal of Eating Disorders,11(1),July2023

    work page

  27. [27]

    Trammelled stars: The non-autonomy of female K-Pop idols.Celebrity Studies,14(4):455–471,June2022

    Laurie Venters and Alexander Rothenberg. Trammelled stars: The non-autonomy of female K-Pop idols.Celebrity Studies,14(4):455–471,June2022

    work page

  28. [28]

    Eating disorder examination.PsycTESTS Dataset,1987

    Zafra Cooper and Christopher Fairburn. Eating disorder examination.PsycTESTS Dataset,1987. [29]Diagnostic and statistical manual of mental disorders: DSM-5. AmericanPsychiatricAssociation,Washington,5thedition, 2013

    work page 1987

  29. [29]

    EDTT: A clinically-grounded and multimodal feature an- notated dataset of pro-eating disorder content on TikTok, 2026

    Eden Shaveet, Thalia Viranda, and Tanzeem Choudhury. EDTT: A clinically-grounded and multimodal feature an- notated dataset of pro-eating disorder content on TikTok, 2026. 6of6 ,

    work page 2026