Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate macroscopical opinion distributions observed in real-world scenarios, their capacity to align with data at the microscopical level remains mostly untested. We evaluate the capacity of the celebrated Voter Model to capture individual opinions in online social networks. We leverage a directed, weighted network of retweets between Twitter (now X) users, collected during the campaign of the 2017 French Presidential Elections. We uncover a strong correspondence between individual opinions in the equilibrium state of the model, and ground-truth party affiliations explicitly stated by the users in their publications and self-descriptions. Users are well separated along party lines in the opinion space of the model, and the model correctly identifies ground-truth party affiliations in 92.5% of cases. We also show that discord probabilities allow us to deduce with high accuracy whether or not two users support the same party. Neither the undirected or unweighted counterparts of the retweet network, nor the follow and mention networks produce comparable results. Our findings highlight the necessity for a fine-grained modelling approach, and contribute to the growing literature on the empirical validity of opinion dynamics models.

On the last French presidential 2022 elections, we collected daily twitters messages on key topics posted by political candidates and their close network. Performing a data driven analysis, we study how political parties interact with one another, measuring key topics on which the candidate had influence over the others.

The literature on whether the internet functions more as an echo chamber, reinforcing users' pre-existing views, or as a diverse forum presenting a multitude of perspectives is ongoing and marked by varied research outcomes. Some studies have identified a tendency for online spaces to foster ideological segregation, suggesting that digital platforms might indeed serve as echo chambers. Conversely, other research indicates that social media platforms, such as Twitter, could offer users exposure to a wider range of news sources and viewpoints than initially thought, challenging the notion of the internet solely as a space of ideological confinement.

Our research aims to deepen the understanding of echo chambers on Twitter/X, addressing gaps in prior studies that have primarily focused on the impact of ideology on information diversity while often overlooking crucial sociodemographic factors. Recognizing that many individuals do not engage deeply with political content, we emphasize the need to expand our inquiry beyond ideological divides. To this end, we employ a representative panel of 1.6 million Twitter/X accounts linked to voter files over four years (building on the approach proposed by Grinberg et al. 2019) and multiple waves of a large national survey (www.covidstates.org). This approach facilitates a comprehensive examination of information consumption and engagement patterns, highlighting the influence of gender, race, and rural living on public discourse and the variety of information accessed. Our objective is to move past the simplistic binary of ideological echo chambers, exploring a broader spectrum of user interactions.

We investigate three key aspects of information consumption on Twitter/X: the diversity of news consumption, the social network dynamics of news sharing, and the structure of user clusters around news sources. By examining whether users are exposed to diverse viewpoints or remain confined to echo chambers that reinforce existing beliefs, we seek to gauge the extent of diverse perspective encounters. Additionally, our analysis of how users cluster around certain news sources and how these clusters vary demographically is pivotal for determining whether Twitter/X serves as a platform for diverse idea exchange or as segmented spaces catering to specific group preferences.

This multifaceted investigation enables us to dissect the complex dynamics of echo chambers on Twitter/X, evaluating the platform's role in either facilitating a broad discourse or contributing to ideological segmentation. Our findings aim to illuminate the role of social media platforms in public discourse, opinion formation, and the vitality of democratic societies, highlighting Twitter's broader implications for societal dialogue and democracy.

As it is well known [1], the influence of media on social opinion does not come from the fact that they succeed in telling people what to think of a given subject but from their success in imposing what people should think about; a situation known as the Agenda Setting Problem. In this way, topics discussed in the public arena are in competition to attract limited people’s attention. In order to model this problem one needs to study two coupled dynamical processes that have comparable time-scales: the evolution of the opinion of the actors, and that of the attention got by the different topics under discussion. Here we propose a multi-dimensional opinion dynamics model inspired by the Axelrod model [2], where each dimension corresponds to a given topic under discussion. Unlike the original model, the contribution of the topics to the overlap that rules social influence among the agents, is neither uniform nor constant. Instead, their relative importance is dynamical, modulated by the attention they attract. The overlap is weighted based on topic popularity, therefore coupled to a process where topics gain or lose attention over time.

We tested the model on stylized networks (Barabási-Albert and Erdős-Rényi) and also on real-world retweet networks of comparable sizes, for various values of the number of features F (here representing the number of topics under discussion), and the number of traits for each feature q (the number of different options the agents can choose for each topic). Preliminary results reveal that the size of the largest opinion cluster and convergence times heavily depend on the choice of the parameters F and q, with lower q and higher F promoting consensus, aligning with previous findings [3].

Competition among topics intensifies with increasing F , making dominance less likely. Moreover, consensus often forms on key features while persistent disagreements on others slow the dynamics. Finally we observe that the network structure significantly impacts the dynamics, leading to distinct outcomes in stylized random and community-structured networks. This work constitutes a new step towards the possibility of comparing theoretical models with empirical studies where the evolution of the attention given to different topics has been measured [4,5].

This paper studies how ideological bias affects the transmission of information on social media. We exploit a novel database combining administrative and Twitter data from a population of French politicians over a two-year period, and study how messages diffuse (i.e. get re-posted and liked) within the sample. Our data show that the network is divided into five distinct communities (`blocks') with internally homogeneous political ideology. We aim at quantifying two ideological biases which may affect information cascades: the `identity' bias against messages originating from different political blocks, and the `topic' bias related to the message content. Our preliminary findings suggest that identity and topic bias are strong yet heterogeneous across political blocks, and that information cascades are based on ideological affinity and topic partisanship.

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.