Public perceptions on Twitter of nurses during the COVID-19 pandemic

Abstract

Background:

The use of social media platforms to convey public opinions and attitudes has exponentially increased over the last decade on topics related to health. In all these social media postings related to the pandemic, specific attention has been focused on healthcare professionals, specifically nurses.

Objective:

This study aimed to explore how the keyword ‘nurse’ is located in COVID-19 pandemic-related tweets during a selected period of the pandemic in order to assess public perception.

Methods:

Tweets related to COVID-19 were downloaded from Twitter for the period January 1st, 2020, to November 11th, 2021. Sentiment analysis was used to identify opinions, emotions, and approaches expressed in tweet which included ‘nurse’, ‘COVID-19’, and ‘pandemic’ as either keyword or hashtags.

Results:

A total of 2,440,696 most used unique words in the downloaded 582,399 tweets were included and the sentiment analysis indicated that 24.4% (n = 595,530) of the tweets demonstrated positive sentiment while 14.1% (n = 343,433) of the tweets demonstrated negative sentiment during COVID-19. Within these results, 17% (n = 416,366) of the tweets included positive basic emotion words of trust and 4.9% (n = 120,654) of joy. In terms of negative basic emotion words, 9.9% (n = 241,758) of the tweets included the word fear, 8.3% (n = 202,179) anticipation, 7.9% (n = 193,145) sadness, 5.7% (n = 139,791) anger, 4.2% (n = 103,936) disgust, and 3.6% (n = 88,338) of the tweets included the word surprised.

Conclusions:

It is encouraging to note that with the advent of major health crises, public perceptions on social media, appears to portray an image of nurses which reflects the professionalism and values of the profession.

Keywords:

• COVID-19
• perception of nurses
• image of nursing
• nurse
• Twitter

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data derived from public domain resources. The data that support the findings of this study are available on Twitter. These data were derived from the following resources available in the public domain: Twitter. R-codes can be made available on request.