This paper provides a simple theoretical framework to evaluate the effect of key parameters of ranking algorithms, namely popularity and personalization parameters, on measures of platform engagement, misinformation and polarization. The results show that an increase in the weight assigned to online social interactions (e.g., likes and shares) and to personalized content may increase engagement on the social media platform, while at the same time increasing misinformation and/or polarization. By exploiting Facebook's 2018 "Meaningful Social Interactions" algorithmic ranking update, we also provide direct empirical support for some of the main predictions of the model.
翻译:本文提供了一个简单的理论框架,用于评估排名算法关键参数(即普及程度和个性化参数)对平台参与、误导和极化等衡量标准的影响。结果显示,对在线社会互动(如喜欢和分享)和个性化内容的权重增加,可能会增加社交媒体平台的参与,同时增加错误和/或极化。我们利用Facebook2018年的“情感社会互动”算法更新,也为模型的一些主要预测提供直接的经验支持。