The field of social network research is moving towards a stronger focus on fairness and polarization. Recent studies have highlighted the importance of ensuring equitable influence distribution across all communities, regardless of protected attributes. Researchers are also exploring the phenomenon of filter bubbles and their negative effects, such as group polarization. Moreover, the quantification of structural polarization in social and information networks has become a critical challenge. Noteworthy papers include: DQ4FairIM, which proposes a fairness-aware influence maximization method using deep reinforcement learning. DSP introduces a statistically-principled structural polarization measure that corrects for biases in existing measures.