JMIR Publications

ABSTRACT

Background:

Discussions of health issues on social media are a crucial source that reflects the real-world response regarding events and opinions. They are often important in public healthcare since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Therefore, artificial intelligence methodologies based on internet search engine queries are suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about healthcare issues, including vaccination and vaccines.

Objective:

Our primary objective is to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal is to define an artificial intelligence-based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may then support adapted vaccination campaigns and would be generalized to other health-related mass communications.

Methods:

The study comprised five stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storing using machine learning techniques; (3) identification of the terms, hashtags, and topics, related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms, topics) to support the understanding of its trends; (5) labeling and evaluating the folksonomy.

Results:

We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, from the USA, and included at least one of the following terms: “flu”, "influenza", “vaccination”, “vaccine”, and “vaxx”. We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that "flu" and "covid" occurrences were inversely correlated as "flu" disappeared over time from the tweets. By combining word embedding and clustering, we have then identified a folksonomy built around three topics dominating the content of the collected tweets: "Health and Medicine [biological and clinical aspects]", "Protection and Responsibility", and "Politics". By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events.

Conclusions:

This study focused initially on vaccination against Influenza and moved de facto to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations' engagement to vaccinate. The greater the likelihood that a targeted population receives a personalized message, the higher is the target population's response, engagement, and proactiveness to the vaccination process.