论文标题
拥挤真相？一个简单的错误信息，两极分化和有意义的社会互动模型
Crowding out the truth? A simple model of misinformation, polarization and meaningful social interactions
论文作者
论文摘要
本文提供了一个简单的理论框架，以评估排名算法的关键参数，即受欢迎程度和个性化参数，对平台参与，错误信息和极化的量度。结果表明，分配给在线社交互动（例如，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上的参与度，同时增加错误信息和/或两极分化。通过利用Facebook的2018年“有意义的社交互动”算法排名更新，我们还为模型的某些主要预测提供了直接的经验支持。
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.
