Mean-field modelling of opinion polarization and structural misinformation susceptibility in online communities

Amplified topic coupling and polarization by higher-order interactions in multidimensional opinion dynamics.

Online discussions often exhibit multidimensional polarization, in which stances on distinct topics become aligned along broader ideological dimensions. Analysis of Reddit data indicates that this cross-topic stance coupling appears within co-participating discussion groups. Motivated by this observation, we introduce a multidimensional opinion dynamics model on a two-layer structure with pairwise edges and hyperedges, together with a group conformity exponent that controls nonlinear group influence. Mean-field and bifurcation analyses reveal two dynamical mechanisms. Linear group conformity lowers the critical threshold at which neutral consensus loses stability and increases cross-topic coupling. Nonlinear group conformity leaves this threshold unchanged but introduces a subcritical bifurcation with a bistable regime. Empirical analysis of data from 27 subreddits spanning four ideological dimensions provides supporting evidence for these mechanisms and shows that the proposed model more closely reproduces observed polarization and coupling patterns than several benchmarks. These results highlight the role of group structures in shaping collective opinion dynamics on online platforms and may offer potential insights for platform governance.

Response-Aware Coarse-Graining in Summary-Mediated Opinion Dynamics

Interacting populations increasingly respond to compressed representations generated from their own collective state. We ask which information such an endogenous coarse-graining must preserve in order to reproduce the collective dynamics. The question has two complementary answers for continuous opinions with attraction, indifference, and repulsion. Inside any strict $K$-bloc response cell where every represented stance attracts every bloc, the dynamics reduce exactly to translation and disagreement. The represented distribution's shape cancels from all disagreement modes, which form a homogeneously anchored weighted signed-Laplacian system, while represented-mean error acts only on translation. This reduction yields a geometry-dependent stability boundary and shows that prevalence bias produces secular drift at marginal stability only through its projection onto the neutral eigenspace. Outside that common linear-response regime, threshold geometry makes distributional information observable. The induced force discrepancy is a behavior-defined integral probability metric; restricted force probes identify generic finite empirical discussions, and for finitely supported candidates uniform force error controls total-variation and Wasserstein error while support reduction incurs a nonzero distortion floor. A cumulative-discrepancy characterization further explains why transport-small summaries can remain behaviorally inaccurate at response thresholds. Mode-resolved integrations, projected particle simulations, finite-size ensembles, and threshold-crossing experiments test these two regimes. As a deployment consequence, broadcast summary error survives as collective noise whereas independently personalized error self-averages. The model is stylized and not empirically calibrated; generated discussion summaries motivate the general problem of endogenous, response-aware coarse-graining.

Quantifying opinion homophily in online social networks from a bounded confidence perspective

Homophily is pervasive in online social media. While many empirical studies have relied on external sociodemographic traits to investigate it, significantly less is known about homophily at the cognitive level, that is, at the level of shared opinions or values. For such “value homophily”, we study interval-based patterns of opinion homophily from a bounded confidence perspective. We consider three heterogeneous datasets from Reddit and Twitter covering polarizing issues, with user opinions quantified via sentiment analysis and fact-checking, and analyze the interaction networks formed by weaker (reply-based) and stronger (follow-based) social ties. Our findings show that users’ interaction neighborhoods are significantly more concentrated in opinion space than expected by chance, with tie strength and issue polarization further amplifying this effect. Moreover, users often exhibit asymmetric tolerance ranges, with asymmetry typically directed toward locally mainstream positions rather than more radical or opposing ones. These findings support a bounded confidence interpretation of value homophily and provide a basis for future studies of exposure and polarization. Homophily in online social media is often examined through external traits, but cognitive-level homophily, based on shared opinions or values, remains underexplored. Here, the authors analyze opinion homophily using opinion estimates based on users’ posts and shared links on Reddit and Twitter, revealing concentrated interaction neighborhoods and asymmetric tolerance, highlighting implications for understanding exposure and polarization dynamics.

Opinion Dynamics for Multidimensional Friedkin–Johnsen Model With Issue Sequences and Switching Topologies

Co-Opetitive Bridging Structure in Rumor Cascades: A Multilayer Overlapping Community Approach with Information-Theoretic Characterization

Rumor events on social media generate opposing camps whose interaction structure is not captured by spreading models or content detectors. This study describes the camp and bridging structure of three rumor events on Sina Weibo, selected from confirmed cases published by the platform’s rumor-refutation channel. Each event is represented as a multilayer interaction network built from repost, comment, and mention relations. Camps are detected by modularity-based community assignment, and overlap is measured through a fractional membership distribution over communities. Three information-theoretic quantities characterize the structure. In the three events, membership entropy separates committed users from bridging users. Structure-to-stance mutual information measures the alignment between interaction communities and text stance. Cross-layer mutual information measures the consistency of camps across interaction types. A co-opetition matrix of mean edge sentiment describes cooperation within camps and competition between camps and identifies alliance structure. In the three events, membership entropy is bimodal, structure-to-stance mutual information is positive and above a permutation null, and the co-opetition matrix has positive diagonal entries. The three events show three distinct temporal patterns, namely a persistent standoff, a hardening toward a single camp after an official correction, and a reversal with an alliance between two camps. The patterns are recovered under a look-ahead-free temporal scheme. In the three events, bridging users hold higher betweenness centrality than non-bridging users. The results describe cross-camp bridging structure in three rumor cascades and connect the structure to a co-opetition reading of camp relations.

Analyzing Public Opinion on International Conflict Through YouTube Comments Using NLP Techniques

The proliferation of user-generated content across social media platforms has fundamentally reshaped the landscape through which public sentiment toward geopolitical events is expressed, contested, and amplified. This study examines public opinion formation and polarization surrounding international armed conflicts by analyzing a corpus of 2.4 million YouTube comments harvested from 847 conflict-related video uploads spanning the 2022–2024 period. Drawing on a multi-layered natural language processing pipeline that integrates transformer-based sentiment classification, topic modeling via Latent Dirichlet Allocation, and graph-theoretic opinion propagation analysis, we demonstrate that comment sections exhibit statistically measurable echo-chamber dynamics — with opinion clusters exhibiting inter-group Jaccard similarity scores below 0.18, markedly lower than intra-cluster baselines. A fine-tuned RoBERTa architecture achieved 91.4% macro-averaged F1 in six-class sentiment categorization, outperforming prior multilingual baselines by a margin of 6.3 percentage points. Temporal analysis further reveals that sentiment volatility spikes within 72 hours of breaking news cycles, before regressing toward regionally distinct equilibria. These findings carry implications for conflict communication scholars, platform governance researchers, and humanitarian information actors seeking to understand the digital public sphere during crises.