2021
DOI: 10.12688/f1000research.51210.3
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An exploratory study of social media users’ engagement with COVID-19 vaccine-related content

Abstract: Background: Facebook, as the world’s most popular social media platform, has been playing various important roles throughout the COVID-19 pandemic, allowing users to produce and share health-related information that both eases and complicates public health communication. However, the characteristics of vaccine-related Facebook content and users’ reaction to the vaccine issue has been an unexplored area to date. Methods: To fill the previous knowledge-gap, this exploratory study wants to understand the communic… Show more

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Cited by 12 publications
(5 citation statements)
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“…For the data crawled with "vaccine," the proportion of positive words (1981/3698, 53.57%) was higher than that of negative words (1717/3698, 46.43%), which revealed that citizens' perceptions of vaccination is somewhat positive. According to a study that examined public perception in Bangladesh based on over 10,000 Facebook posts using "vaccine" as the keyword [22], the proportion of citizens who regarded vaccination positively (74.61%) was similar to this study's findings. Of the positive words used in the posts, "nice" was most regularly used (13.4%), followed by "treatment" (9.9%), "health" (9.3%), "safety" (9.3%), "prevention" (6.0%), "recovery" (3.4%), and "hope" (2.4%).…”
Section: Principal Findingssupporting
confidence: 82%
“…For the data crawled with "vaccine," the proportion of positive words (1981/3698, 53.57%) was higher than that of negative words (1717/3698, 46.43%), which revealed that citizens' perceptions of vaccination is somewhat positive. According to a study that examined public perception in Bangladesh based on over 10,000 Facebook posts using "vaccine" as the keyword [22], the proportion of citizens who regarded vaccination positively (74.61%) was similar to this study's findings. Of the positive words used in the posts, "nice" was most regularly used (13.4%), followed by "treatment" (9.9%), "health" (9.3%), "safety" (9.3%), "prevention" (6.0%), "recovery" (3.4%), and "hope" (2.4%).…”
Section: Principal Findingssupporting
confidence: 82%
“…93 In Bangladesh, Facebook users mostly reacted to vaccinerelated posts positively. 94 Interestingly, the Iranian Twitter users reported similar rates of positive and negative emotions towards foreign and homegrown vaccines explored between April and September 2021 (43 vs 40% were positive and 45 vs 40% were negative, for foreign and domestic vaccines, respectively). 95 The USA general public sentiment showed a decrease in negative sentiment during the first two months after starting the vaccination (after December 2020) with a total of one-third negatively oriented tweets.…”
Section: Countries December 2020 -March 2021mentioning
confidence: 90%
“…Analysis of the infodemic and the digital conversation on twitter [Desinformacio ´n, vacunas y covid-19. Ana ´lisis de la infodemia y la conversacio ´n digital en twitter] 137 [186] Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study 138 [187] Youtube as a source of information on epidural steroid injection 139 [188] An exploratory study of social media users' engagement with COVID-19 vaccine-related content 140 [189] Online influencers: Healthy food or fake news 141 [190] Sentimental Analysis on Health-Related Information with Improving Model Performance using Machine Learning 142 [191] Digital civic participation and misinformation during the 2020 taiwanese presidential election 143 [192] Challenging post-communication: Beyond focus on a 'few bad apples' to multi-level public communication reform 144 [193] Knowledge about COVID-19 in Brazil: Cross-sectional web-based study 145 [194] "Down the rabbit hole" of vaccine misinformation on youtube: Network exposure study 146 [195] Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection 147 [196] Social Media Use by Young People Living in Conflict-Affected Regions of Myanmar 148 [197] Two-Path Deep Semisupervised Learning for Timely Fake News Detection 149 [198] Deep learning for misinformation detection on online social networks: a survey and new perspectives 150 [199] FauxWard: a graph neural network approach to fauxtography detection using social media comments 151 [200] Internet users engage more with phatic posts than with health misinformation on Facebook 153 [202] Partisan public health: how does political ideology influence support for COVID-19 related misinformation?…”
Section: Id Document Referencementioning
confidence: 99%