Conference Agenda

As signified in the phrase, ‘no issue, no public,’ attention to shared issues brings strangers together. In today’s increasingly fragmented issue landscape, establishing a common understanding of issues becomes particularly crucial for social cohesion. This study investigates what promotes issue overlap between individuals engaged in personalized news curation.

Through stochastic actor-oriented modeling (SAOM) with digital log data and survey data, we investigate underlying mechanisms leading to the formation of shared-issue networks during South Korea’s presidential election. We also compare network dynamics between individuals with low and high involvement in politics, examining how the election’s increased issue salience and meta-narrative catalyzed joint interest differently across involvement levels.

Our findings reveal that the likelihood of forming shared-issue relations increases over time, when individuals are less susceptible to political homophily, accumulate greater political knowledge, and practice manual filtering to increase exposure to diverse news genres. Notably, specialized issue interests tend to develop from general interests, rather than vice versa. Individuals became involved in specific issues based on their broader understanding of related contexts, highlighting the significance of cultivating genre-level news interests and ensuring diversified genre exposure in news consumption patterns.

This research extends scholarly discourse beyond personalized news consumption to issue sharing mechanisms by shifting the focus from the individual level to the network level—an approach rarely taken in public opinion formation literature. It offers insights into the evolving public discourse landscape shaped by both low-and-high involvement citizens. Our findings also contribute to a deeper understanding of the current information dynamics.

This study explores the relational and discursive dynamics of the COVID-19 vaccine controversy on YouTube French content between 2020 and 2023, using a network analysis approach. It distinguishes between production arenas (subscription networks between channels) and consumption arenas (shared commenter networks) to uncover structural patterns. Key concepts such as clusters, centrality, and inter- and intra-cluster relationships are examined.

An innovative method, the disparity filter algorithm (Serrano et al. 2009), is employed to reduce graph density and reveal the network's "backbone." This approach enhances the readability and interpretability of large-scale digital networks, addressing key challenges in social network analysis.

By conceptualizing YouTube as a public arena, this study pragmatically moves beyond conventional filter bubble and echo chamber theories. Instead, it investigates how discourses are structured, circulated, and interact in algorithmically shaped environments. Comparing articulated (subscription-based) and behavioral (comment-based) networks, we examine their convergence and divergence in structuring online controversies.

Results reveal distinct thematic clusters, where institutional and scientific channels form relatively closed communities, while critical and alternative narratives interact more dynamically. The reduction of the commenter network exposes cross-cluster interactions, identifying key mediation zones where conflicting discourses meet. These findings suggest that controversy on YouTube is not purely polarized but structured around differentiated arenas with varying degrees of permeability.

Therefore, this research contributes to understanding the organization of digital controversies. It offers both theoretical and methodological insights into how network analysis can reveal the underlying structures of online debates, particularly in contentious scientific and political issues.

Survey data provide strong evidence that the average number of close social connections has increased over the past two decades. Simultaneously, societal opinions have become increasingly polarized. To explore whether these trends are connected, we use a multidimensional opinion dynamics model that realistically captures both homophily and social balance.

In this model, individuals interact dyadically, yet triad statistics consistent with social balance theory emerge naturally. We find a phase transition where, at critical connectivity of the underlying social network, a rapid transition from practically no to strong polarization occurs.

By understanding how increased social connectivity necessarily leads to polarization we

discuss strategies to mitigate polarization in highly connected societies.

This study introduces TIDEM (Twitter Ideological Distance Estimation Method) a novel methodology for measuring ideological distances and evaluating political polarization using Twitter retweet networks. By using network-based analysis and spatial proximity within ForceAtlas2 layouts, the method captures ideological dynamics and provides a complementary perspective to traditional approaches such as self-placement surveys, the Chapel Hill Expert Survey (CHES), and Manifesto analysis. The methodology is applied to three Spanish regional elections (Catalonia and Madrid 2021, and Andalusia 2022) revealing consistent results through the three cases. A cross-election comparative demonstrates consistency in the relative positioning of left-right ideological blocks and the two major national parties (PP-PSOE). When evaluating TIDEM against traditional methods, the results indicate a strong correlation with self-ideological surveys across all three elections, except for the positioning of Cs in Madrid. However, comparisons with CHES and Manifesto data show mixed outcomes. Additionally, the analysis highlights the importance of regional context in shaping party positions, particularly in multi-dimensional ideological scenarios like Catalonia. Key findings indicate that TIDEM shows higher levels of polarization, likely due to the clustering effects inherent in retweet interactions. While traditional methods tend to position parties more centrally and show reduced distances between ideological blocks, our approach underscores the fluidity of public sentiment and the amplifying effects of online discourse. These results accentuate the potential of social media data as a valuable, scalable, and cost-effective source. TIDEM provides a relevant methodology for studying ideological distances and polarization. While it cannot replace traditional methods, it serves as a powerful complement.

Opinion dynamics models are a versatile tool for investigating how a network of politically affiliated agents form perceptions collectively about media bias under exogenous (independent analysis of media outputs) and endogenous influences (peer pressure). Previous numerical studies of these models show that persuadable agents in politically allied networks are disrupted from asymptotically learning (learning in the long run) the true bias of a media organization, when the network is populated by one or more obdurate agents (partisans), who are not persuadable themselves but exert peer pressure on other agents. Partisan disruption occurs in two ways: agents asymptotically learn a false bias, or agents' opinions never settle and vacillate indefinitely between belief in a false bias and the true bias, a phenomenon called turbulent nonconvergence. In this paper, we derive (and validate with Monte Carlo simulations) an analytic instability condition that distinguishes these two modes of partisan disruption, in terms of the learning rate and key network properties, for an idealized model of media bias featuring a biased coin. We interpret the condition as expressing a balance between the exogenous influence of the media organization’s published outputs, and the endogenous influence of the partisans. We explore the partisan influence as a function of network size, sparsity, and partisan fraction and find that the network is less likely to experience turbulent nonconvergence as the learning rate increases, size decreases, sparsity increases, and partisan fraction increases. These results and their social implications are interpreted briefly in terms of the social science theory of structural balance.