Semester 2, academic year 2023/2024
By Ellen Linnert
Despite its popularity, YouTube has gained a reputation for fostering toxic communication through its affordances (Alexander, 2018; Munn, 2020). This echoes general concerns that social media sites could hinder deliberative democratic communication processes (Pfetsch, 2020). However, the role of social media in facilitating certain discourse dynamics remains unclear. Algorithms that have the power to structure discourse on a platform and to encourage expressive behaviors by ranking user comments (e.g., YouTube’s “top comment” algorithm) are still greatly understudied.
Specifically divisive expressions – outraged references to one’s partisan out-group within a moral context (e.g., accusing the out-group of violating moral norms) – might fuel social divides. These highly emotional comments that impose a moralization of political conflict can obstruct productive deliberation while also being highly engaging to the platform users (Brady et al., 2017, 2021; Rathje et al., 2021). This poses a potential dilemma for the algorithmic ranking of these comments. Therefore, this study investigates the role of divisive expressions in the algorithmic ranking of and engagement with user comments on YouTube news content.
To tackle challenges of big social media datasets and language processing, the study employs state-of-the-art computational methods using pre-trained large language models. This way, elements of divisive expressions can be detected automatically in large quantities of user comments on US-focused news content. By assessing the relationship between divisive expressions and the algorithmic ranking of user comments on a large scale, this study contributes to the understanding of algorithmic affordances and public discourse online.