The Impact of Visual Abstracts Compared to Automated Tweets on Social Media in Otology & Neurotology.

Document Type

Article

Publication Date

6-1-2024

Publication Title

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology

Abstract

OBJECTIVE: To analyze the effect of visual abstracts versus automated tweets on social media participation in Otology & Neurotology .

PATIENTS: N/A.

INTERVENTIONS: Introduction of visual abstracts developed by the social media editorial team to established automated tweets created by the dlvr.it computer program on the Otology & Neurotology Twitter account.

MAIN OUTCOME MEASURES: Twitter analytics including the number of new followers per month, impressions per tweet, and engagements per tweet. The Kruskal-Wallis analysis of variance test was used to compare means.

RESULTS: From October 2016 to October 2017 (average of 20 new followers per month), 101 automated tweets averaged 536 impressions and 16 engagements per tweet. The visual abstract was introduced in November 2017. From November 2017 to November 2020 (average of 39 new followers per month), 447 automated tweets averaged 747 impressions and 22 engagements per tweet, whereas 157 visual abstracts averaged 1977 impressions and 78 engagements per tweet. Automated tweets were discontinued in December 2020. From December 2020 to December 2022 (average of 44 new followers per month), 95 visual abstracts averaged 1893 impressions and 103 engagements per tweet. With the introduction of the visual abstract, the average number of followers, impressions per tweet, and engagements per tweet significantly increased (all p -values

CONCLUSIONS: Visual abstracts created by a social media editorial team have a positive impact on social media participation in the field of otology and neurotology. The impact is greater than that of social media content generated by Twitter automation tools.

Volume

45

Issue

5

First Page

e363

Last Page

e365

DOI

10.1097/MAO.0000000000004186

ISSN

1537-4505

PubMed ID

38626773

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