The piece
Petrovskaya E, Khoo N, Xiao LY, Leahy D, Roberts A. “Gambling adverts on social media reach 2.3 times more men than women: Using the Meta Ad Library to assess gambling advertising in Ireland.” Journal of Behavioral Addictions (2026). doi.org/10.1556/2006.2025.00484
Researchers used the Meta Ad Library, a public ad repository the EU’s Digital Services Act now requires large platforms to maintain, to examine 411 gambling adverts from 88 licensed operators in Ireland. The question was not only who the ads were aimed at, but who actually saw them.
My take
The usual worry about gambling advertising is intent: that operators deliberately aim at the people least able to absorb the harm. What makes this study worth sitting with is that it found the harm-shaped pattern without the intent. Only about a fifth of the ads explicitly targeted men, and none targeted women alone; most were simply set to reach everyone. Yet men were reached more than twice as often as women, and most of that gap came from ads that never asked to be shown to men. The skew was produced by the platform’s delivery algorithm, not by the advertiser’s targeting.
That distinction matters more than it first appears. The system was not handed a list of the vulnerable; it assembled one as a by-product of chasing engagement. In Ireland, men aged 25 to 34 carry the highest rate of problem gambling of any group, and that is almost exactly the group these ads reached most. What is striking is that the delivery concentrated exposure in the very demographic already known to be at highest risk, without any operator having asked it to.
Key Insight
An engagement-optimizing system doesn’t need to target the vulnerable; it finds them on its own, because to the algorithm, vulnerability and engagement look identical.
I would resist the cleanest version of this story, though, because the mechanism is a loop rather than a push. The algorithm is not seeking out vulnerable men out of malice; it is optimizing for engagement, and men engage more with gambling ads, so the system learns to send them more. The vulnerability and the exposure feed each other. That is, in a way, worse than deliberate targeting, because there is no single decision to regulate away — and it is exactly why the authors are right that banning explicit gender targeting would not fix this. A skew the algorithm generates on its own survives a rule aimed only at what operators consciously ask for. The delivery mechanism itself is the thing that needs looking at. The limits are worth stating plainly. This is one country, a small slice of all the gambling ads in circulation, and platform data that researchers cannot independently verify. The numbers also predate Ireland’s 2024 gambling law, which since March 2025 restricts these ads to users who opt in, so the snapshot is of a pre-regulation moment rather than the present. And the data cannot tell us whether the men reached were new recruits or existing customers being pulled back in. None of that softens the observation that travels well beyond Ireland: when exposure follows engagement, the people who get the most of it are often the ones who can least afford it.