Michigan Senate candidate Abdul El-Sayed said that the Instagram algorithm frequently places videos from OnlyFans models in his social media feed [1, 2].
The comments highlight a growing tension between how users perceive automated content delivery and how social media platforms actually track user interaction to serve recommendations.
El-Sayed discussed the nature of his "For You" page and the role of his staff in managing his digital presence. He said that his team has access to his account and interacts with the content he views [1].
"You're on Instagram," El-Sayed said. "My team has access to my Instagram account and like the things that I actually look at on Instagram, right? Of course we get served that on your 'For You' page, but then like every third video that scrolls on its own is going to be some OnlyFans model" [1].
Critics of the candidate argue that the presence of such content is not a random algorithmic error. They said the recommendations are a direct result of the interaction history of El-Sayed and his team [1, 2].
Under the current design of Instagram's recommendation system, the platform analyzes likes, views, and shares to determine what a user is most likely to engage with. This creates a feedback loop where specific types of content are prioritized based on previous behavior, a process the candidate attributed to the algorithm itself [1, 2].
El-Sayed did not provide specific details on how long this has been occurring or if he has attempted to filter the content. The discussion has surfaced as part of a broader conversation regarding the transparency of social media algorithms and their influence on the information users consume during political campaigns [1].
“"Every third video that scrolls on its own is going to be some OnlyFans model."”
This incident underscores the 'filter bubble' effect, where the distinction between an algorithm's independent choice and a user's behavioral input becomes blurred. For a political candidate, the public nature of these recommendations can become a liability, as social media feeds are often interpreted by critics as a reflection of a user's private interests rather than a technical byproduct of platform engagement.
