Algorithms do not neutrally present information. Instead, they continuously observe user behaviour and recalibrate what becomes visible. When millions of users log onto a platform simultaneously, who is seen and what is seen is determined not by the content itself, but by algorithmic interpretations of interaction signals. As Princeton computer science professor Arvind Narayanan notes, when we speak online, when we share ideas, write posts, or upload images and videos, who hears those voices depends largely on algorithmic systems. Visibility, therefore, is no longer a passive outcome of publication, but something actively produced through ongoing interaction. At the level of platform mechanisms, algorithms sever the direct relationship between speakers and audiences. Whether speech circulates is no longer determined by the speaker or their social network, but by whether content can be repeatedly activated within the platform. Browsing, doubting, commenting, saving, and sharing are all recognised as meaningful signals and fed back into recommendation systems. Visibility thus takes the form of a loop: content is seen, interaction occurs, and interaction in turn reinforces visibility.

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Under these conditions, negative evaluations do not naturally disappear because of their critical tone. On the contrary, they often persist longer precisely because they invite comparison, scepticism, and response. When users pause, debate, or return to these posts, they participate in an invisible process of production that enables content to continue circulating. This repeated activation constitutes what can be described as an algorithmic visibility loop. Different users enter and sustain this loop in different ways. Some continue consuming despite criticism, others visit in order to verify claims, while some translate disappointment into new expressions. Regardless of the path taken, interaction itself becomes the force that maintains circulation and prepares the ground for subsequent value conversion.

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On content platforms such as RED, visibility does not depend on whether an evaluation is positive or negative, but on whether it can be repeatedly triggered. Algorithms do not distinguish between agreement, rebuttal, or doubt; they register only that interaction has occurred. As long as content continues to be opened, discussed, and saved, it enters broader recommendation pathways.

 

Over time, negative evaluations detach from their original function as judgements and become part of a cycle composed of attention, interaction, and redistribution. Under this logic, negative content does not fade away. Instead, it is repeatedly amplified by platform mechanisms, forming stable pathways of algorithmic visibility.