Evidence map›Paper›PMID 37672310›Full record

ArticleJMIR formative research2023

Fact-Checking Cancer Information on Social Media in Japan: Retrospective Study Using Twitter.

Nari Kureyama, Mitsuo Terada, Maho Kusudo, Kazuki Nozawa, Yumi Wanifuchi-Endo, Takashi Fujita, Tomoko Asano, Akiko Kato, Makiko Mori, Nanae Horisawa and 1 more

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  13. Cancer: A model topic for misinformation researchers.Current opinion in psychology · 2024
    Review
  14. Article
  15. Cancer misinformation on social media.CA: a cancer journal for clinicians
    Review
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

11 authors.

Nari KureyamaDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0009-0007-3966-6829
Mitsuo TeradaDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0000-0002-1304-2619
Maho KusudoDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0009-0008-0604-2566
Kazuki NozawaDepartment of Breast Oncology, Aichi Cancer Center Hospital, Nagoya, Japan.ORCID https://orcid.org/0000-0002-0277-8196
Yumi Wanifuchi-EndoDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0000-0002-0703-2289
Takashi FujitaDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0009-0000-6578-086X
Tomoko AsanoDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0009-0002-7929-3848
Akiko KatoDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0009-0009-8157-7533
Makiko MoriDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0000-0003-3427-2461
Nanae HorisawaDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0000-0001-8457-5781
Tatsuya ToyamaDepartment of Breast Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.ORCID https://orcid.org/0000-0003-4713-7795

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients' decision-making is also disseminated on social media platforms.

objectiveWe aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer.

methodsUsing the Twitter app programming interface, we extracted tweets containing the term "cancer" in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of "likes." For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information.

resultsThe extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most "likes" that met the eligibility criteria, 44 (44%) contained misinformation, 31 (31%) contained harmful information, and 30 (30%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4%), Disproven (19/94, 20.2%), Inappropriate application (4/94, 4.3%), Strength of evidence mischaracterized (14/94, 14.9%), Misleading (18/94, 18%), and Other misinformation (1/94, 1.1%). Harmful action was described as Harmful action (9/59, 15.2%), Harmful inaction (43/59, 72.9%), Harmful interactions (3/59, 5.1%), Economic harm (3/59, 5.1%), and Other harmful information (1/59, 1.7%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002).

conclusionsIt is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions.

Indexed as

cancerfact-checkmisinformationsocial mediatwitter

Identifiers

PMID37672310
PMCPMC10512120

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.