Frustration with mainstream social media platforms and their content moderation decisions have prompted many users to search for "anti-censorship" alternatives, such as those in alt-tech, which may be used to share extreme or hateful content. The tension in alt-tech between limiting perceived censorship and reducing hate speech means that content moderation policies are essential but also controversial. Nevertheless, alt-tech content moderation policies are understudied. To address this research gap, we leverage quasi-experimental design to measure the impact of an "incitement to hatred" policy change using 5.2 million comments and the metadata of 800 thousand videos from the alt-tech platform BitChute. We uncover evidence for a "backlash effect," finding that after the implementation of the policy, hate speech increased significantly for comments and video metadata. This study contributes to the literature on content moderation policies in a challenging context where users may not be receptive to perceived impositions.

Political communication is critical for democracy, but polarized emotions in communication may make careful deliberation difficult. Much of modern political communication occurs on social media, which may exacerbate these challenges. This study examines how the design of social media features impact political communication. We examined how the introduction of Facebook Reactions influenced the posts of the 114th US Congress on the platform. We start by analyzing the emotional content of posts, finding that politicians generally increased their usage of negative emotions in their posts after the feature’s launch. Further analysis showed that increased user engagement preceded the rise in negative emotions, suggesting that politicians were making adjustments based on user feedback. Our results show that the design features of social media can shape online political communication.

Graph neural networks (GNNs) have been widely used for recommender systems over knowledge graphs. An important issue of GNN-based recommender systems is individual user fairness in recommendations (i.e., similar users should be treated similarly by the systems). In this paper, we make the following contributions to enable recommender systems to be equipped with individual user fairness. First, we define new similarity metrics for individual fairness, where these metrics take knowledge graphs into consideration by incorporating both first-order proximity in direct user-item interactions and second-order proximity in knowledge graphs. Second, we design a novel graph neural network (GNN) named SKIPHop for fair recommendations over knowledge graphs. By passing latent representations from both first-order and second-order neighbors at every message passing step, SKIPHop learns user embed dings that capture their latent interests present in the second-order networks. Furthermore, to realize individual user fairness, we add fairness as a regularization to the loss function of recommendation models. Finally, through experiments on two real-world datasets, we demonstrate the effectiveness of SKIPHop in terms of fairness and recommendation accuracy.