Evidence map›Paper›PMID 39652864›Full record

ArticleJournal of medical Internet research2024

Alternative Health and Conventional Medicine Discourse About Cancer on TikTok: Computer Vision Analysis of TikTok Videos.

Roxana Mika Muenster, Kai Gangi, Drew Margolin

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Cancer Education in the Age of TikTok and Social Media: Opportunity or Misinformation?Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Roxana Mika MuensterDepartment of Communication, Cornell University, Ithaca, NY, United States.ORCID 0000-0002-5052-5660
Kai GangiDepartment of Computer Science, Cornell University, Ithaca, NY, United States.ORCID 0009-0004-0411-0167
Drew MargolinDepartment of Communication, Cornell University, Ithaca, NY, United States.ORCID 0000-0003-0257-6217

Funding

Using Natural Language Processing and Crowdsourcing to Monitor and Evaluate Public Information and Communication Disparities about Colon Cancer ScreeningR37CA259156 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Andy J King · 2021 to 2026
$2.3M
NCI NIH HHS R37 CA259156
6 · The paper itself

Abstract

backgroundHealth misinformation is abundant online and becoming an increasingly pressing concern for both oncology practitioners and patients with cancer. On social media platforms, including the popular audiovisual app TikTok, the flourishing alternative health industry is further contributing to the spread of misleading and often harmful information, endangering patients' health and outcomes and sowing distrust of the medical community. The prevalence of false and potentially dangerous treatments on a platform that is used as a quasi-search engine by young people poses a serious risk to the health of patients with cancer.

objectiveThis study seeks to examine how cancer discourse on TikTok differs between alternative health and conventional medicine videos. It aims to look beyond mere facts and falsehoods that TikTok users may utter to understand the visual language and format used in the support of both misleading and truthful narratives, as well as other messages.

methodsUsing computer vision analysis and subsequent qualitative close reading of 831 TikTok videos, this study examined how alternative health and conventional medicine videos on cancer differ with regard to the visual language used. Videos were examined for the length of time and prominence in which faces are displayed, as well as for the background setting, location, and dominant color scheme.

resultsThe results show that the alt-health and conventional health samples made different use of the audiovisual affordances of TikTok. First, videos from the alternative health sample were more likely to contain a single face that was prominently featured (making up at least 7.5% of the image) for a substantial period of time (35% of the shots), with these testimonial-style videos making up 28.5% (93/326) of the sample compared to 18.6% (94/505) of the conventional medicine sample. Alternative health videos predominantly featured cool tones (P<.001) and were significantly more likely to be filmed outdoors (P<.001), whereas conventional medicine videos were more likely to be shot indoors and feature warm tones such as red, orange, or yellow.

conclusionsThe findings of this study contribute to an increased understanding of misinformation as not merely a matter of individual falsehoods but also a phenomenon whose effects might be transported through emotive as well as rational means. They also point to influencer practices and style being an important contributing factor in the declining health of the information environment around cancer and its treatment. The results suggest that public health efforts must extend beyond correcting false statements by injecting factual information into the online cancer discourse and look toward incorporating both visual and rational strategies.

Indexed as

NeoplasmsComplementary TherapiesHumansSocial MediaVideo Recordingalternative healthcancercomputer visionmisinformationsocial mediaTikTok

Identifiers

PMID39652864
PMCPMC11667741

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.