  The Covid-19 pandemic has sparked renewed attention on the prevalence of
misinformation online, whether intentional or not, underscoring the potential
risks posed to individuals' quality of life associated with the dissemination
of misconceptions and enduring myths on health-related subjects. In this study,
we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social
media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major
news sources - both questionable and reliable. We first use the symbolic
transfer entropy analysis of news production time-series to dynamically
determine which category of sources, questionable or reliable, causally drives
the agenda on vaccines. Then, leveraging deep learning models capable to
accurately classify vaccine-related content based on the conveyed stance and
discussed topic, respectively, we evaluate the focus on various topics by news
sources promoting opposing views and compare the resulting user engagement.
Aside from providing valuable resources for further investigation of
vaccine-related misinformation, particularly in a language (Italian) that
receives less attention in scientific research compared to languages like
English, our study uncovers misinformation not as a parasite of the news
ecosystem that merely opposes the perspectives offered by mainstream media, but
as an autonomous force capable of even overwhelming the production of
vaccine-related content from the latter. While the pervasiveness of
misinformation is evident in the significantly higher engagement of
questionable sources compared to reliable ones, our findings underscore the
importance of consistent and thorough pro-vax coverage. This is especially
crucial in addressing the most sensitive topics where the risk of
misinformation spreading and potentially exacerbating negative attitudes toward
vaccines among the users involved is higher.
