Evidence map›Paper›PMID 31226342›Full record

ArticlePreventive medicine2019

Facts or stories? How to use social media for cervical cancer prevention: A multi-method study of the effects of sender type and content type on increased message sharing.

Jingwen Zhang, Gem Le, David Larochelle, Rena Pasick, George F Sawaya, Urmimala Sarkar, Damon Centola

Open access · greenAbstract read
In one paragraph

Article in Preventive medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 5% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
  2. Social Media Peer-Driven Communication on Cervical Cancer and HPV Vaccine Among Female Youth in Nigeria.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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 at 3 institutions in 1 country.

Jingwen ZhangUniversity of California, Davis, Department of Communication, One Shields Avenue, Davis, CA 95616, USA.
Gem LeUniversity of California San Francisco, Center for Vulnerable Populations, Division of General Internal Medicine, San Francisco, CA 94143, USA.
David LarochelleUniversity of Pennsylvania, Annenberg School for Communication, 3620 Walnut Street, Philadelphia, PA 19104, USA.
Rena PasickUniversity of California San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94143, USA.
George F SawayaUniversity of California San Francisco, Department of Obstetrics and Gynecology, San Francisco, CA 94143, USA.
Urmimala SarkarUniversity of California San Francisco, Center for Vulnerable Populations, Division of General Internal Medicine, San Francisco, CA 94143, USA.
Damon CentolaUniversity of Pennsylvania, Annenberg School for Communication, 3620 Walnut Street, Philadelphia, PA 19104, USA. Electronic address: dcentola@asc.upenn.edu.
University of California, San Francisco · USUniversity of Pennsylvania · USUniversity of California, Davis · US

Funding

Influencing cervical cancer prevention and detection online through social mediaR01CA178875 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CENTOLA, DAMON, SARKAR, URMIMALA · 2014 to 2016
$1.3M
NCI NIH HHS R01 CA178875
6 · The paper itself

Abstract

Social media has become a valuable tool for disseminating cancer prevention information. However, the design of messages for achieving wide dissemination remains poorly understood. We conducted a multi-method study to identify the effects of sender type (individuals or organizations) and content type (personal experiences or factual information) on promoting the spread of cervical cancer prevention messages over social media. First, we used observational Twitter data to examine correlations between sender type and content type with retweet activity. Then, to confirm the causal impact of message properties, we constructed 900 experimental tweets according to a 2 (sender type) by 2 (content type) factorial design and tested their probabilities of being shared in an online platform. A total of 782 female participants were randomly assigned to 87 independent 9-person online groups and each received a unique message feed of 100 tweets drawn from the 4 experimental cells over 5 days. We conducted both tweet-level and group-level analyses to examine the causal effects of tweet properties on influencing sharing behaviors. Personal experience tweets and organizational senders were associated with more retweets. However, the experimental study revealed that informational tweets were shared significantly more (19%, 95% CI: 11 to 27) than personal experience tweets; and organizational senders were shared significantly more (10%, 95% CI: 3 to 18) than individual senders. While rare personal experience messages can achieve large success, they are generally unsuccessful; however, there is a reproducible causal effect of messages that use organizational senders and factual information for achieving greater peer-to-peer dissemination.

Indexed as

Consumer Health InformationEarly Detection of CancerInformation DisseminationFemaleHealth PromotionHumansResearch DesignSocial MediaUterine Cervical NeoplasmsCervical cancer preventionDisseminationMulti-method studySocial mediaTweets

Identifiers

PMID31226342
PMCPMC6697580
OpenAlexW2952391563

What OpenQuestion holds

Textmetadata
LicenceTDM
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.