Many scholars have proposed that polarization is driven by the human penchant to seek engagement with like-minded others (e.g., social identification approach of group polarization, echo chamber hypothesis). We will discuss the merits of this view, but also explore other factors that might explain polarization phenomena, such as the role of media and media perceptions, the impact of cognitive and metacognitive processes on polarization, or the question of whether explicitly expressing dissent can lead to polarization.

Despite some efforts to conceptualize and measure polarization as a state, the majority of scholars regards polarization as a complex process that unfolds over time. Similarly, research on misinformation has taken a particular interest in how these types of information disseminate across social networks. To capture these dynamics, traditional empirical methods (experiments, surveys) are less suited, thus giving rise to advanced methods like using large field data or computational modeling. We will discuss different data-driven approaches, and how they can improve our understanding of polarization and related phenomena.

A variety of mechanisms have been suggested to mitigate the spread of a polarized landscape and to contain the effects of misinformation (e.g., platform regulations, fostering media literacy, nudging/boosting). We will discuss the effectiveness of these strategies and introduce some novel techniques (game-based inoculation, acquisition of flexibility mindsets). Ideally, our discussions on mitigation help us to develop a portfolio of scientifically sound strategies that can be communicated to various stakeholders.