Is Digital Polarization a natural extension of the Physical Divide

Network analysis demonstrates how social infrastructures and the groups to which individuals belong significantly influence their behaviors. The strength of a group's influence on its members is contingent upon the group's relevance in a particular context. As illustrated in Homophily and the Focused Organization of Ties, a person's behavior is shaped by both their individual traits and the focal points of the groups they are part of. These traits and group foci influence each other, and neither completely determines an individual's behaviors or orientations.

People naturally belong to multiple groups, both voluntarily and involuntarily. For instance, being born in a country automatically associates an individual with that country's group. This logic extends to other affiliations, such as those related to schools, socio-economic class, age, and gender. Even when membership in certain groups—like a political group—seems voluntary, as discussed in A Macrosociological Theory of Social Structure, there is often a strong correlation among the parameters of various groups. This correlation increases the likelihood that individuals in one group (e.g., conservatives) will also belong to another group (e.g., republicans).

Social infrastructure plays a crucial role in determining group membership. For example, the experiences of young mothers in [Courses/Cornell/Sem 2/SNT/Readings/Homophily and the Focused Organization of Ties|Homophily and the Focused Organization of Ties] reveal that their limited exposure to differing parenting styles was not intentional but rather a consequence of the groups they were already part of. This reasoning can also be applied to people's political stances, particularly regarding pressing issues such as immigration or abortion. Individuals' beliefs are predominantly shaped by the groups they belong to, and these groups are determined by social infrastructures.

Factors such as the class divide, which is palpable in the physical world, further exacerbate the distance between groups, increasing ideological divides. While social media algorithms have often been criticized for accelerating political polarization, my readings suggest that this growing polarization may be more closely related to our evolving social structures than to the algorithms themselves. As noted in Quantifying social organization and political polarization in online platforms, the increase in polarization is largely attributed to the influx of new polarized users rather than changes in existing users or algorithmic adjustments.

Moreover, social media platforms are characterized by network effects, where individuals are more likely to use a platform if others in their network are already using it. This phenomenon can be seen as an extension of the physical world. It raises the question of whether this trend explains the concentration of different types of users across various platforms—where a majority of right-leaning users gravitate toward Truth Social, Elon Musk's patrons favor X, and left-leaning users migrate to Bluesky. This leads me to wonder if it is possible to create a digital space that is not polarized without addressing the growing inequality and divide present in the physical world.