Thursday 5 January 2023

Can misinformation really impact people’s intent to get vaccinated?

Health misinformation, especially that related to vaccines, is not a new phenomenon. Growth of the internet in the last two decades has led to an exponential rise in use of online social media platforms. Consequently, many people rely on information available online to inform their health decisions. While social media platforms have improved access to all kinds of information for their users, false information has been shown to diffuse faster and deeper than true information. Therefore, it’s not surprising that the COVID-19 pandemic has been accompanied by an “infodemic”: an excessive spread of false or misleading information, both online and offline, that has eroded trust in scientific evidence and expert advice, and undermined the public health response. Susceptibility to online misinformation regarding the pandemic has been negatively associated with compliance of public health guidance, and willingness to get vaccinated. However, it is not clear if it is exposure to misinformation that lowers vaccination intent, or simply that those who are unwilling to vaccinate believe in (mis)information that justifies their stance on vaccination.

This begs the question, can exposure to misinformation actually lower people’s intent to get vaccinated? Given the hidden underlying factors that can induce a correlation between belief in misinformation and unwillingness to vaccinate, we conducted a randomized controlled experiment in September 2020 in the UK and the USA on a representative sample of 4,000 people from each country. Everyone was asked about their intention to get a COVID-19 vaccine to protect themselves. Two-thirds of the people were then exposed to five recently circulating pieces of online misinformation about COVID-19 vaccines, whereas one-third were exposed to five pieces of factually correct information to serve as a control group. Following this, people were asked again about their vaccination intent. This allowed us to quantify the causal impact of exposure to misinformation, relative to factual information, on vaccination intent. Before exposure, 54.1% of respondents in the UK and 42.5% in the USA reported that they would “definitely” accept a COVID-19 vaccine, while 6.0% and 15.0% said they would “definitely not” accept it. Unfortunately, even this brief exposure to misinformation induced a decline in intent of 6.2 percentage points in the UK and 6.4 percentage points in the USA among those who stated that they would definitely accept a vaccine. Interpreting these results in the context of vaccination coverage rates required to achieve herd immunity—which ranges from anywhere between 55% to 85% depending on the country and infection rate—suggests that misinformation could impede efforts to successfully fight the pandemic.

Randomized controlled experiment reveals that exposure to misinformation can reduce the willingness to vaccinate oneself against COVID-19, which may be detrimental to goals of achieving herd immunity.

Since now we know that COVID-19 misinformation can indeed reduce people’s willingness to vaccinate, what can be done about it? We must understand the how and why of it. There exist a small minority of organized actors who supply much of this misinformation online, but the vast majority of people do not actively seek to spread misinformation. They are simply trying to make an informed decision while faced with a deluge of information, and false narratives that exploit people’s fear and anxieties simply become more likely to be shared ahead. In our research (in Nature Human Behaviour), we found that scientific-sounding misinformation that purported a direct link between the COVID-19 vaccine and adverse effects were associated more strongly with a decline in intent. This extends the scope of the problem beyond online misinformation, and towards understanding and addressing vaccine hesitancy more broadly. Individually, we must understand the concerns of our peers, and think about veracity over emotions before sharing anything with them, online or offline. Collectively, we must foster a deeper public understanding of vaccination, which can be brought about by clearer scientific communication, and rebuilding trust in institutions. Sahil

Wednesday 4 January 2023

Inference and influence of network structure using snapshot social behavior without network data

In a more and more polarized world, the role of underling social structure driving social influence in opinions and behaviours is still unexplored. In this study we developed a method, the kernel-Blau-Ising model (KBI), to uncover how people are influenced/connected based on their socio-demographic coordinates (e.g., income, age, education, postcode), and tested the model in the EU referendum and two London Mayoral elections.

Outline of the KBI methodology (high res image here). Input data consist of aggregated behavioral data for different geographical areas and sociodemographic variables (age, income, education, etc.) associated to those areas (from census data). (A) Heatmap of (hypothetical) behavioral data in Greater London, in this case electoral outcomes, where red represents 100% votes to Labour and blue represents 100% votes to Conservatives. (B) Probability distribution of behavioral outcomes in (A). (C) Blau space representation of the behavioral outcomes spanned by sociodemographic characteristics (e.g., age and income). (D) Blau space representation of KBI approach using input data in and learning parameters: the External Fields, which account for the general trends, e.g., older people are more likely to vote Conservatives than younger people, and the network that connects the population according to their distances in the Blau space and their homophilic preferences. Once the model parameters are learnt, we can further estimate how changes and interventions affect behavioral outcomes. Examples of potential network-sensitive intervention strategies: how changes to income distribution (E) and homophilic preferences (F) can reduce behavioral polarization.

Despite using no social network data, we discover established signatures of homophily, the tendency to befriend those similar to oneself—the stronger homophily is, the more social segregation. We found consistent geographical segregation for the three elections, while education was a strong segregation factor for the EU Referendum, it wasn’t for Mayoral Elections, however, age and income were. The model can be used to explore how reducing inequalities or encouraging mixing among groups can reduce social polarization. You can read about our work "Inference of a universal social scale and segregation measures using social connectivity kernels" free in the journal Science Advances here. Antonia and Nick

Stochastic Survival of the Densest: defective mitochondria could be seen as altruistic to understand their expansion

With age, our skeletal muscles (e.g. muscle of our legs and arms) work less well. In some people, there is a substantial loss of strength an...