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Retention has provided another encouraging signal. Depending on the market, Yashin says day-one, day-seven and day-30 retention can range from around the crash-game benchmark to between five and 10 times higher. The number of rounds played during a session can also be significantly greater.

The explanation, he believes, comes back to the behaviour that inspired Swipe Games in the first place. Crash players may experience a different outcome each time, but visually they repeatedly encounter much the same thing. Social media has conditioned its users to expect something new with every swipe.

“People are not playing it just as a quick crash game,” Yashin says. “They play it more like they are betting. It is a different pattern.”

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The proposal establishes criteria for classifying products according to their potential for harm. Among the characteristics considered are instantaneous or short-lived results, continuous repetition at short intervals, use of random mechanisms to determine the outcome, intermittent rewards, near-miss incentives, incentives to recover losses and features that make it difficult to stop betting or induce successive, impulsive, or increasingly valuable bets. 

Products offered to the public must undergo prior evaluation by a competent body of the Federal Executive Branch, to be defined in regulations. Products classified as high-risk will be subject to specific harm reduction measures. Products with excessive risk may not be offered. This category includes products with outcomes determined by random mechanisms, continuous cycles and variable rewards, such as roulette, slot machines, collision games and simulated virtual sports. 

Furthermore, the text maintains obligations for monitoring and institutional cooperation, with the provision of aggregated and anonymised data to the competent authorities. It also provides for actions by the executive branch aimed at monitoring the impacts of betting, training health professionals, updating care protocols and periodically disseminating information on the effects of the activity. 

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According to the survey, the problem arises when a user follows a single account related to betting. The platform’s recommendation system then starts showing other profiles in the same segment, including betting sites, influencers and specialised pages.

Among the 100 most recommended profiles are accounts that, together, have accumulated more than 6 million followers and 114,000 posts. There are three main types of profiles recommended by Instagram: operators, content creators and media specialising in the betting market.

The research also identified content that associates betting with investments and the possibility of income.