ABSTRACT

Online algorithms have received much blame for polarizing emotions during the 2016 U.S. presidential election. We use transfer entropy to measure directed information flows from human emotions to YouTube’s video recommendation engine, and back, from recommended videos to users’ emotions. We find that algorithmic recommendations communicate a statistically significant amount of positive and negative affect to humans. Joy is prevalent in emotional polarization, while sadness and fear play significant roles in emotional convergence. These findings can help to design more socially responsible algorithms by starting to focus on the emotional content of algorithmic recommendations. Employing a computational-experimental mixed method approach, the study serves as a demonstration of how the mathematical theory of communication can be used both to quantify human-machine communication, and to test hypotheses in the social sciences.

摘要

在2016年美国总统大选期间，在线算法因两极分化而备受指责。我们使用传递熵来衡量从人类情感到YouTube视频推荐引擎以及从推荐视频到用户情感的定向信息流。我们发现算法建议传达了对人类正面和负面影响的统计意义。欢乐在情感上的两极分化中很普遍，而悲伤和恐惧在情感融合中起着重要的作用。通过开始关注算法推荐的情感内容，这些发现可以帮助设计更具社会责任感的算法。采用计算实验混合方法的方法，