Fake AI celebrity endorsements of quack wellness cures are surging among the elderly

Paris Hilton’s mother Kathy recently tried the “Jell-O diet” to lose weight. She believed other celebrities – Oprah Winfrey, Doctor Oz, Michelle Obama and Kelly Clarkson – had endorsed it. But the diet was fake and so were the endorsements. Both had been generated by artificial intelligence (AI).

Commentators were quick to poke fun. One said:

Kathy is everyone’s grandma that you can’t keep off the internet reading crazy things.

Misleading health claims are easy to find online, not just about fake diets. Examples include misinformation about vaccines and the cholesterol-lowering drugs statins. Then there’s the growing online market for unapproved peptides.

Health misinformation – false, misleading, exaggerated or unsupported claims about health, disease and medical treatments – can have consequences.

It can delay people from seeking medical care, discourage them from following evidence-based treatment, increase preventable illness and erode trust in health professionals. This can happen if such health information is shared deliberately to mislead or with the best of intentions.

And it’s easy to think older people – like 67-year-old Kathy – are more easily tricked into believing it.

But we’re all susceptible, whatever our age.

Kathy Hilton explained what happened to her body when she went on the fake ‘Jell-O diet’.

Are older people really more at risk?

Adults aged 60 or older are more likely than younger adults to be exposed to deceptive or untrustworthy health content or sites, particularly via Facebook.

This includes content aimed at selling products, lacks sources for health claims or appears to be AI-generated.

The researchers concluded:

no other demographic characteristic examined – gender, income, education – had any consistent relationship with the likelihood of sharing fake news.

It’s tempting to attribute this to age-related cognitive decline. But adults aged 60 or older can distinguish true headlines from false ones, for example.

So differences in online behaviour are unlikely to be explained by ageing alone.

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So, what’s going on?

People are more likely to believe health misinformation if they follow conspiracy theories, are religious, and follow conservative ideologies or parties. Distrusting science, or being worried or anxious, also increases the risk of becoming susceptible to health misinformation.

Some people are exposed to more misinformation than others. This includes those who actively engage with unreliable content by commenting on it or sharing it. Being exposed to health misinformation repeatedly can also make false claims appear accurate.

Many of these behavioural and psychological factors apply to younger adults too.

But older adults use and interact with online health information and misinformation in different ways.

When they visit a “dodgy” health website, that’s mainly via other low-credibility sites (such as business and shopping sites), rather than through search engines or social media.

They also face challenges younger generations may not. Many join social media later in life and have had fewer opportunities to develop skills to evaluate digital information.

And even if they do end up encountering health information online, they don’t rely on it as their primary source. They often go online to clarify medical advice, answer everyday health questions or fill gaps after a consultation. For health information, most still trust doctors, pharmacists and family members.

What can we do about it?

Combating health misinformation requires more than teaching people, of any age, how to spot a fake headline. Health and digital literacy programs should help people:

  • choose reliable sources before engaging with questionable claims
  • recognise common tactics such as emotional language, false experts and conspiracy narratives
  • pause before liking or sharing.

These efforts should be tailored to different audiences and reinforced over time. A one-off checklist may improve people’s ability to detect dubious content, but is less likely to produce lasting changes in what people believe or how they behave.

Even high levels of digital literacy or experience online are no guaranteed immunity to healthy misinformation. This can make older people think they’re better than they are at spotting misleading information online. Older people might also be sceptical or cynical about various sources, but engage with it anyway.

The platforms have a role too

Search engines and social media platforms also need to do more to help people of all ages evaluate the information they encounter.

Examples could include integrating real-time credibility indicators, such as a traffic light system of warning labels. These can be effective across different demographics, and languages.

Search engines and social media platforms could also present evidence from government websites next to online claims to allow readers to make a side-by-side comparison.

So rather than removing misleading content or adding fact-check labels after the fact, these approaches encourage users to compare information before reaching a conclusion.

Let’s not blame older people

Ultimately, tackling health misinformation is not simply about teaching people of any age to “use the internet better”. It requires helping them select reliable sources, recognise how misinformation exploits emotions and shapes beliefs, and pause before sharing.

This is while creating online environments where trustworthy health information is easier to find and recognise.

These strategies should be tailored to people’s existing digital skills, reinforcing good habits among experienced users while providing more in-built support to those with less experience.

Holly Seale is a social scientist and Professor at the School of Population Health, University of New South Wales at Sydney. Find Holly on X @hollyseale

Saiful Islam is a social epidemiologist and Senior Lecturer at the School of Population Health, Faculty of Medicine and Health, University of New South Wales at Sydney.

A version of this article was originally posted at Conversation and has been reposted here with permission. Any reposting should credit the original author and provide links to both the GLP and the original article. Find Conversation on X @ConversationUS

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