BlogPost 040

Abstract: Selective exposure—the tendency for individuals to seek information that confirms their beliefs and avoid contradictory content—plays a significant role in shaping public opinion. In political contexts, it is often measured on a left-right ideological scale, which misses important complexities. In the first stage of my research, combining survey and Twitter data from Brazil’s 2022 Presidential Election, I explore selective exposure among survey respondents and political influencers, revealing a hierarchical, multilevel community structure within the followership network that goes beyond simple ideological divides. This structure fosters Echo Chambers, where individuals’ opinions are reinforced through interactions with like-minded users, with the likelihood of such chambers forming potentially varying by community level. Additionally, viewing Political Polarization through a multilevel lens highlights dimensions beyond ideology, including issue-based polarization and affective polarization tied to group identities. Understanding these dynamics in selective exposure, echo chambers, and polarization is essential for developing strategies to mitigate social media’s influence on public opinion and democracy. 

Abstract: Computational social science is an interdisciplinary domain that involves both active social scientists and methodologists. As complex factors increasingly influence human behavior in the social media age, there is an urgent need for social scientists to adopt advanced methods from computer science and network science to analyze emerging social phenomena. Meanwhile, computer and network scientists highlight the need to update methodologies to better align with real-world data in a transferring societal environment. However, there remains a lack of connections between the two domains. I will share my experience about collaborating across disciplines as an interdisciplinary researchers and discuss the opportunities and challenges involved.