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As the last few years have seen an increase in online hostility andpolarization both, we need to move beyond the fack-checking reflex or thepraise for better moderation on social networking sites (SNS) and investigatetheir impact on social structures and social cohesion. In particular, the roleof recommender systems deployed at large scale by digital platforms such asFacebook or Twitter has been overlooked. This paper draws on the literature oncognitive science, digital media, and opinion dynamics to propose a faithfulreplica of the entanglement between recommender systems, opinion dynamics andusers' cognitive biais on SNSs like Twitter that is calibrated over a largescale longitudinal database of tweets from political activists. This modelmakes it possible to compare the consequences of various recommendationalgorithms on the social fabric and to quantify their interaction with somemajor cognitive bias. In particular, we demonstrate that the recommendersystems that seek to solely maximize users' engagement necessarily lead to anoverexposure of users to negative content (up to 300\% for some of them), aphenomenon called algorithmic negativity bias, to a polarization of the opinionlandscape, and to a concentration of social power in the hands of the mosttoxic users. The latter are more than twice as numerous in the top 1\% of themost influential users than in the overall population. Overall, our findingshighlight the urgency to identify harmful implementations of recommendersystems to individuals and society in order better regulate their deployment onsystemic SNSs.

Can Few Lines of Code Change Society ? Beyond fack-checking and moderation : how recommender systems toxifies social networking sites