Evidence map›Paper›PMID 34900400›Full record

ArticleDigital journalism (Abingdon, England)2021

How Message Features and Social Endorsements Affect the Longevity of News Sharing.

Hyun Suk Kim

Abstract readNews
In one paragraph

Article in Digital journalism (Abingdon, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

1 author.

Hyun Suk KimDepartment of Communication, Seoul National University, Seoul, South Korea.

Funding

Tobacco Control in a Rapidly Changing Media EnvironmentU01CA154254 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI EMERY, SHERRY L · 2011 to 2015
$7.7M
Constructing Recommender Systems for Effective Health Messages: Smoking CessationR01CA160226 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CAPPELLA, JOSEPH NICHOLAS · 2011 to 2014
$980k
NCI NIH HHS R01 CA160226NCI NIH HHS U01 CA154254
6 · The paper itself

Abstract

This study examined how message features and social endorsements affect the longevity of audience news sharingby analyzing behavioral data of social retransmission of New York Timeshealth news articles, and associated article content and context data.The results showed that information utility-related message features increased the duration for which articlespromptedemail-based sharing, whereas emotional positivity and controversiality increased the longevity of social media-based sharing. Expressed emotional evocativeness and the absence of death-related words lengthened the duration for which articles prompted both email- and social media-based sharing. Newsretransmission, viaeither email or social media, was more likely to persist when the articles stayed on the "most-emailed" list for a longer time, showingsocial endorsement-driven cumulative advantage effects. The results further revealed synergistic interaction effects between social endorsements and message features. While social endorsements produced strong cumulative-advantage effects on the longevity of news sharing, articles with certain message features that are diagnostic of their inherent share-worthiness generated even stronger effects than those articles that appeared on the "most-emailed" list for the same amount of time but without such features. These features were expressed emotional evocativeness (email-based sharing), the absence of death-related words and exemplification (social media-based sharing).

Indexed as

longevitymessage effectsmessage featuresnews diffusionnews sharingsocial endorsementsvirality

Identifiers

PMID34900400
PMCPMC8654353

What OpenQuestion holds

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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.