Search

Artificial intelligence (AI) is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having marked effects on global health. Here, we evaluate whether recruited individuals can distinguish disinformation from accurate information, structured in the form of tweets, and determine whether a tweet is organic or synthetic, i.e., whether it has been written by a Twitter user or by the AI model GPT-3. The results of our preregistered study, including 697 participants, show that GPT-3 is a double-edge sword: In comparison with humans, it can produce accurate information that is easier to understand, but it can also produce more compelling disinformation. We also show that humans cannot distinguish between tweets generated by GPT-3 and written by real Twitter users. Starting from our results, we reflect on the dangers of AI for disinformation and on how information campaigns can be improved to benefit global health.

Background: The recent introduction of COVID-19 certificates in several countries, including the introduction of the European green pass, has been met with protests and concerns by a fraction of the population. In Italy, the green pass has been used as a nudging measure to incentivize vaccinations because a valid green pass is needed to enter restaurants, bars, museums, or stadiums. As of December 2021, a valid green pass can be obtained by being fully vaccinated with an approved vaccine, recovered from COVID-19, or tested. However, a green pass obtained with a test has a short validity (48 hours for the rapid test, 72 hours for the polymerase chain reaction test) and does not allow access to several indoor public places., Objective: This study aims to understand and describe the concerns of individuals opposed to the green pass in Italy, the main arguments of their discussions, and their characterization., Methods: We collected data from Telegram chats and analyzed the arguments and concerns that were raised by the users by using a mixed methods approach., Results: Most individuals opposing the green pass share antivaccine views, but doubts and concerns about vaccines are generally not among the arguments raised to oppose the green pass. Instead, the discussion revolves around the legal aspects and the definition of personal freedom. We explain the differences and similarities between antivaccine and anti-green pass discourses, and we discuss the ethical ramifications of our research, focusing on the use of Telegram chats as a social listening tool for public health., Conclusions: A large portion of individuals opposed to the green pass share antivaccine views. We suggest public health and political institutions to provide a legal explanation and a context for the use of the green pass, as well as to continue focusing on vaccine communication to inform vaccine-hesitant individuals. Further work is needed to define a consensual ethical framework for social listening for public health., (©Giovanni Spitale, Nikola Biller-Andorno, Federico Germani. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.02.2022.)

Vaccinations are without doubt one of the greatest achievements of modern medicine, and there is hope that they can constitute a solution to halt the ongoing COVID-19 pandemic. However, the anti-vaccination movement is currently on the rise, spreading online misinformation about vaccine safety and causing a worrying reduction in vaccination rates worldwide. In this historical time, it is imperative to understand the reasons of vaccine hesitancy, and to find effective strategies to dismantle the rhetoric of anti-vaccination supporters. For this reason, we analyzed the behavior of anti-vaccination supporters on the platform Twitter. Here we identify that anti-vaccination supporters, in comparison with pro-vaccination supporters, share conspiracy theories and make use of emotional language. We demonstrate that anti-vaccination supporters are more engaged in discussions on Twitter and share their contents from a pull of strong influencers. We show that the movement's success relies on a strong sense of community, based on the contents produced by a small fraction of profiles, with the community at large serving as a sounding board for anti-vaccination discourse to circulate online. Our data demonstrate that Donald Trump, before his profile was suspended, was the main driver of vaccine misinformation on Twitter. Based on these results, we welcome policies that aim at halting the circulation of false information about vaccines by targeting the anti-vaccination community on Twitter. Based on our data, we also propose solutions to improve the communication strategy of health organizations and build a community of engaged influencers that support the dissemination of scientific insights, including issues related to vaccines and their safety., Competing Interests: The authors have declared that no competing interests exist.