Algorithmic Filtering, Out-Group Stereotype, and Polarization on Social Media

The introduction of social media websites touted the idea of global communication - exposing users to a worldwide audience and a diverse range of experiences, opinions, and debates. Unfortunately, studies have shown that social networks have instead contributed to growing levels of polarization in society across a wide variety of issues. Social media websites employ algorithmic filtering strategies to drive engagement, which can lead to the formation of filter bubbles and increased levels of polarization. In this paper, we introduce features of affective polarization - feelings towards one's in-group and out-group - into an opinion dynamics model. Specifically, we show that incorporating a negative out-group stereotype into the opinion dynamics model (1) affects the level of polarization present among agents in the network; (2) changes the effectiveness of algorithmic filtering strategies; and (3) is exacerbated by the presence of extremists in the network. Hence, the inclusion of an affective group mechanism in opinion dynamics modeling provides novel insights into the effects of algorithmic filtering strategies on the extremity of opinions in social networks.