A multilingual dataset of COVID-19 vaccination attitudes on Twitter

Exploring Topics, Emotions, and Sentiments in Health Organization Posts and Public Responses on Instagram: Content Analysis

BACKGROUND

Social media is a vital tool for health organizations, enabling them to share evidence-based information, educate the public, correct misinformation, and support a more informed and healthier society.

OBJECTIVE

This study aimed to categorize health organizations' content on social media into topics; examine public engagement, sentiment, and emotional responses to these topics; and identify gaps in fear between health organizations' messages and the public response.

METHODS

Real data were collected from the official Instagram accounts of health organizations worldwide. The BERTopic algorithm for topic modeling was used to categorize health organizations' posts into distinct topics. For each identified topic, we analyzed the engagement metrics (number of comments and likes) of posts categorized under the same topic, calculating the average engagement received. We examined the sentiment and emotional content of both posts and responses within the same topic, providing insights into the distributions of sentiment and emotions for each topic. Special attention was given to identifying emotions, such as fear, expressed in the posts and responses. In addition, a linguistic analysis and an analysis of sentiments and emotions over time were conducted.

RESULTS

A total of 6082 posts and 82,982 comments were collected from the official Instagram accounts of 8 health organizations. The study revealed that topics related to COVID-19, vaccines, and humanitarian crises (such as the Ukraine conflict and the war in Gaza) generated the highest engagement. Our sentiment analysis of the responses to health organizations' posts showed that topics related to vaccines and monkeypox generated the highest percentage of negative responses. Fear was the dominant emotion expressed in the posts' text, while the public's responses showed more varied emotions, with anger notably high in discussions around vaccines. Gaps were observed between the level of fear conveyed in posts published by health organizations and in the fear conveyed in the public's responses to such posts, especially regarding mask wearing during COVID-19 and the influenza vaccine.

CONCLUSIONS

This study underscores the importance of transparent communication that considers the emotional and sentiment-driven responses of the public on social media, particularly regarding vaccines. Understanding the psychological and social dynamics associated with public interaction with health information online can help health organizations achieve public health goals, fostering trust, countering misinformation, and promoting informed health behavior.

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Social media platforms, such as Twitter, have been paramount in the COVID-19 context due to their ability to collect public concerns about the COVID-19 vaccination campaign, which has been underway to end the COVID-19 pandemic. This worldwide campaign has heavily relied on the actual willingness of individuals to get vaccinated independently of the language they speak or the country they reside. This study analyzes Twitter posts about Pfizer/BioNTech, Moderna, AstraZeneca/Vaxzevria, and Johnson & Johnson vaccines by considering the most spoken western languages. Tweets were sampled between April 15 and September 15, 2022, after the injections of at least three doses, collecting 9,513,063 posts that contained vaccine-related keywords. To determine the success of vaccination, temporal and sentiment analysis have been conducted, reporting opinion changes over time and their corresponding events whenever possible concerning each vaccine. Furthermore, we have extracted the main topics over languages providing potential bias due to the language-specific dictionary, such as Moderna in Spanish, and grouped them per country. Once performed the pre-processed procedure we worked with 8,343,490 tweets. Our findings show that Pfizer has been the most debated vaccine worldwide, and the main concerns have been the side effects on pregnant women and children and heart diseases.