Evidence map›Paper›PMID 35275077›Full record

Observational studyJournal of medical Internet research2022

The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study.

Congning Ni, Zhiyu Wan, Chao Yan, Yongtai Liu, Ellen Wright Clayton, Bradley Malin, Zhijun Yin

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
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  8. Article
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

7 authors.

Congning NiDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0001-6950-6948
Zhiyu WanDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0003-3752-5778
Chao YanDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0002-6719-1388
Yongtai LiuDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0002-0279-3644
Ellen Wright ClaytonCenter for Genetic Privacy & Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID 0000-0002-0308-4110
Bradley MalinDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0003-3040-5175
Zhijun YinDepartment of Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID 0000-0002-3075-1337

Funding

Genetic Privacy and Identity in Community Settings - GetPreCiSeRM1HG009034 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CLAYTON, ELLEN WRIGHT, MALIN, BRADLEY A. · 2016 to 2024
$8.3M
NHGRI NIH HHS RM1 HG009034
6 · The paper itself

Abstract

backgroundIn November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as #GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood.

objectiveThe aim of this study is to examine web-based posts about the #GeneEditedBabies event and characterize and compare the public's stance across social media platforms with different user bases.

methodsWe used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms.

resultsWe collected >220,000 posts published by approximately 130,000 users regarding the #GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9%), followed by Sina Weibo (968/1140, 84.91%), Reddit (550/898, 61.2%), and YouTube (567/1078, 52.6%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the #GeneEditedBabies event.

conclusionsThis research provides evidence that posts on web-based platforms can offer insights into the public's stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers.

Indexed as

HIV InfectionsSocial MediaChinaHumansInfant, NewbornPublic Opinioncontent analysisCRISPR/Cas9gene-edited babiessocial mediastance learningtext mining

Identifiers

PMID35275077
PMCPMC8957000

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.