Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 19, 2024
Date Accepted: Dec 18, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Optimising public health communication: an analysis of type, sentiment and source of COVID-19 tweets
ABSTRACT
Background:
Social media can be used to quickly disseminate focused public health messages, increasing message reach and interaction with the public. Social media can also be an indicator of people's emotions and concerns. Social media data text mining can be used for disease forecasting and understanding public awareness of health-related concerns. Limited studies explore the impact of type, sentiment and source of tweets on engagement.
Objective:
To determine the association between message type, user (source) and sentiment of Covid-19 twitter posts and public engagement.
Methods:
867,485 tweets were extracted from 1 Jan 2020 to 31 Mar 2022 from Ireland and the UK. A four-step analytical process was undertaken, encompassing sentiment analysis, bio-classification, message classification and statistical analysis. Manual coding and machine learning coding models were used to categorise sentiment, user category and message type for every tweet. A zero-inflated negative binomial model was applied to explore the most engaging content mix.
Results:
Our analysis resulted in 12 user categories, 6 messages categories and 3 sentiment classes. Personal stories and positive messages have the most engagement, even though not for every user group; known persons and influencers have the most engagement with humorous tweets. Health professionals receive more engagement with advocacy, personal stories/statements and humour based tweets. Health Institutes observe higher engagement with advocacy, personal stories/statements and tweets with a positive sentiment. This study suggests the optimum mix of message type and sentiment that each user category should use to get more engagement.
Conclusions:
Our study provides valuable guidance for social media based public health campaigns for developing messages for maximum engagement
Citation
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Copyright
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