Title

What Makes a Tweet Fly? Analysis of Twitter Messaging at Four Infection Control Conferences

Author Faculty (Discipline)

Nursing

Document Type

Article

Publication Date

11-2017

Publication Details

This article was originally published as:

Mitchell, B. G., Russo, P. L., Otter, J. A., Kiernan, M. A., & Aveling, L. (2017). What makes a tweet fly? Analysis of twitter messaging at four infection control conferences. Infection Control & Hospital Epidemiology, 38(11), 1271-1276. doi: 10.1017/ice.2017.170

ISSN: 1559-6834

ANZSRC / FoR Code

100599 Communications Technologies not elsewhere classified| 111002 Clinical Nursing: Primary (Preventative)| 111711 Health Information Systems (incl. Surveillance)| 111716 Preventive Medicine

Avondale Research Centre

Lifestyle Research Centre

Reportable Items

C1

Abstract

Objective: To examine tweeting activity, networks, and common topics mentioned on Twitter at four international infection control and infectious disease conferences.

Design: A cross-sectional study.

Methods: An independent company was commissioned to undertake a Twitter ‘trawl’ each month between July 1, 2016, and November 31, 2016. The trawl identified any tweets that contained the official hashtags of the conferences for (1) the UK Infection Prevention Society, (2) IDWeek 2016, (3) the Federation of Infectious Society/Hospital Infection Society, and (4) the Australasian College for Infection Prevention and Control. Topics from each tweet were identified, and an examination of the frequency and timing of tweets was performed. A social network analysis was performed to illustrate connections between users. A multivariate binary logistic regression model was developed to explore the predictors of ‘retweets.’

Results: In total, 23,718 tweets were identified as using 1 of the 2 hashtags of interest. The results demonstrated that the most tweets were posted during the conferences. Network analysis demonstrated a diversity of twitter networks. A link to a web address was a significant predictor of whether a tweet would be retweeted (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.9–2.1). Other significant factors predicting a retweet included tweeting on topics such as Clostridium difficile (OR, 2.0; 95% CI, 1.7–2.4) and the media (OR, 1.8; 95% CI, 1.6–2.0). Tweets that contained a picture were significantly less likely to be retweeted (OR, 0.06; 95% CI, 0.05–0.08).

Conclusion: Twitter is a useful tool for information sharing and networking at infection control conferences.

Comments

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