Evidence map›Paper›PMID 31086737›Full record

ArticlePeerJ2019

Likes, comments, and shares of marine organism imagery on Facebook.

Craig R McClain

Abstract read
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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.

Craig R McClainLouisiana Universities Marine Consortium, Chauvin, LA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several calls to action urge scientists and science communicators to engage more with online communities. While these calls have been answered by a high percentage of scientists and science communicators online, it often remains unclear what are the best models for effective communication. Best practices and methods for online science communication can benefit from experimental and quantitative research addressing how and when users engage with online content. This study addresses with quantitative and predictive models a key question for the popular, but often-ignored in science communication, social media platform Facebook. Specifically, this study examines the impact of imagery through quantification of likes, comments, and shares on Facebook posts. Here, I show that a basic quantitative model can be useful in predicting response to marine organism imagery on Facebook. The results of this online experiment suggest image type, novelty, and aesthetics impact the number of likes, shares, and comments on a post. In addition, the likes, shares, and comments on images did not follow traditional definitions of "charismatic megafauna", with cephalopods and bony fishes receiving more interactions than cartilaginous fishes and marine mammals. Length and quality of caption did not significantly impact likes, comments, or shares. This study provides one of the first quantitative analysis of virality of scientific images via social media. The results challenge previously held conceptions of social media scientific outreach including increasing emphasis on imagery selection and curation, notions of which taxa the public connect with, and role of captions for imagery.

Indexed as

FacebookScience communicationSocial mediaVirality

Identifiers

PMID31086737
PMCPMC6486811

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.