Users online tend to join polarized groups of like-minded peers around shared narratives, forming echo chambers. The echo chamber effect and opinion polarization may be driven by several factors including human biases in information consumption and personalized recommendations produced by feed algorithms. Until now, studies have mainly used opinion dynamic models to explore the mechanisms behind the emergence of polarization and echo chambers. The objective was to determine the key factors contributing to these phenomena and identify their interplay. However, the validation of model predictions with empirical data still displays two main drawbacks: lack of systematicity and qualitative analysis. In our work, we bridge this gap by providing a method to numerically compare the opinion distributions obtained from simulations with those measured on social media. To validate this procedure, we develop an opinion dynamic model that takes into account the interplay between human and algorithmic factors. We subject our model to empirical testing with data from diverse social media platforms and benchmark it against two state-of-the-art models. To further enhance our understanding of social media platforms, we provide a synthetic description of their characteristics in terms of the model's parameter space. This representation has the potential to facilitate the refinement of feed algorithms, thus mitigating the detrimental effects of extreme polarization on online discourse.

翻译：在线用户倾向于围绕共享叙事加入志同道合的同质化群体，形成回声室。回声室效应与观点极化可能由多种因素驱动，包括人类在信息消费中的认知偏差以及推荐算法产生的个性化推送。迄今为止，研究主要采用观点动态模型探索极化和回声室形成背后的机制，旨在确定导致这些现象的关键因素并识别其相互作用。然而，模型预测与经验数据的验证仍存在两大缺陷：缺乏系统性和定性分析。本研究通过提供一种数值比较方法填补这一空白，该方法可将模拟得到的观点分布与社交媒体上观测到的观点分布进行对比。为验证该流程，我们构建了一个考虑人类与算法因素相互作用的观点动态模型，利用来自不同社交媒体平台的数据进行实证检验，并与两种前沿模型进行基准比较。为深化对社交媒体平台的理解，我们基于模型参数空间对其特征进行了综合描述。这一表征有望促进推荐算法的优化，从而减轻极端极化对在线话语的负面影响。