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How neighbourhood ideology shapes misinformation belief in densely tied social networks

Soroush Karimi, Marcos Oliveira, Diogo Pacheco

arXiv:2609.10277v1cs.SIcs.MAphysics.soc-ph

TL;DR

Previous frameworks failed to account for claims’ ideological alignment. The paper introduces a contagion model that incorporates individuals’ ideological intensity, claim alignment, and social influence into belief formation. Dense network clustering amplifies neighbours’ ideological influence, and even small populations of highly ideological agents can increase misinformation adoption among neighbouring moderates.

  • Problem

    Previous frameworks failed to account for claims’ ideological alignment.

    Source: Conclusion · p. 8

  • Method

    The paper introduces a contagion model that incorporates individuals’ ideological intensity, claim alignment, and social influence into belief formation.

    Source: The Ideological Misinformation… · p. 3, Conclusion · p. 8, Model · p. 10

  • Results

    Dense network clustering amplifies neighbours’ ideological influence, and even small populations of highly ideological agents can increase misinformation adoption among neighbouring moderates.

    Source: Conclusion · p. 8

  • Takeaways & Limitations

    Accounting for individual biases and network structure gives a more plausible view of misinformation propagation.

    Source: Conclusion · p. 8

    The model considers only a single dimension of ideology and its alignment with a claim.

    Source: Conclusion · p. 9

Abstract

from arXiv · show

With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

The Ideological Misinformation Contagion Model

The model combines individuals’ ideological predispositions with social influence to describe how a single false claim spreads or is rejected across a network. It distinguishes personal ideological intensity from claim alignment and incorporates both into belief transitions.

The Ideological Misinformation Contagion Model

  • The contagion model integrates individual ideological intensity, claim alignment, and social influence into misinformation propagation.It modifies the SBFC framework by incorporating the interaction between personal intensity and how closely a claim aligns with an individual’s ideology.Source: The Ideological Misinformation… · pp. 3–4
  • Claim alignment is a property of the misinformation, while ideological intensity is an individual-level attribute.This distinction lets the same individual respond differently to different claims according to their ideological alignment.Source: The Ideological Misinformation… · pp. 3–4
  • Individuals transition among susceptible, believer, and fact-checker states through local social influence and their ideological relationship to the claim.The model describes propagation and rejection of a single false claim over a social network.Source: The Ideological Misinformation… · p. 4

Results

The results show that misinformation belief reflects both personal ideological predispositions and neighbours’ influence, with neighbourhood influence strengthened in highly clustered networks. Claim alignment and individual ideological intensity shape belief responses, while the size of those effects varies with gullibility and network structure.

Results

  • The model’s results indicate that ideological intensity and social influence jointly shape misinformation propagation.Neighbour influence becomes more important in highly clustered networks.Source: Results · p. 4

Greater Ideological Alignment Leads to More Believers

  • When claim alignment is neutral, ideological intensity does not affect the believer percentage; aligned claims increase it, while opposing claims decrease it.Under low gullibility (α = 0.05), few moderate individuals believe and greater ideological intensity is needed; higher gullibility increases belief among moderates even at low intensity.Source: Greater Ideological Alignment Leads to… · pp. 4–5

Individual Belief Responses Vary with Claim Ideological Alignment

  • For aligned claims, extremists are more likely to believe, and moderates connected to extremist neighbours also have higher belief probabilities.For opposing claims, extremists predominantly become fact-checkers and have low belief probabilities; for neutral claims, moderates and extremists behave similarly.Source: Individual Belief Responses Vary with… · p. 6

Network Clustering Amplifies the Influence of Neighbourhood Ideology

  • Neighbour influence becomes much more significant in networks where neighbouring nodes are highly interconnected, as indicated by higher clustering coefficients; Facebook has an average clustering coefficient of 0.606.Source: Network Clustering Amplifies the… · p. 7
  • In highly clustered networks, neighbours’ ideological intensity can influence misinformation belief as much as personal ideological bias.The influence is amplified by repeated local interactions constrained by network topology, and can increase adoption among neighbouring moderates.Source: Network Clustering Amplifies the… · p. 8

Conclusion

The conclusion argues that misinformation adoption reflects the interaction of personal ideology, claim alignment, and network structure. Clustering magnifies neighbourhood ideological influence, while the model considers only a single ideological dimension.

Conclusion

  • The contagion model integrates individual ideological intensity and claim alignment with social influence, and simulations examine these factors across network structures.The model treats ideological intensity as an individual attribute and claim alignment as a property of misinformation.Source: Conclusion · p. 8, Figure 5 · p. 10
  • Unbiased individuals behave similarly across claim alignments, whereas claims opposing extremist values are debunked and aligned claims are believed.Source: Conclusion · p. 8
  • Neighbour ideology has greater influence in highly clustered networks, where small highly ideological populations can significantly increase misinformation adoption among neighbouring moderates.The amplification arises from the interaction between ideological alignment and network topology, rather than being directly encoded in the transition rules.Source: Conclusion · pp. 8–9, Figure 4 · p. 9
  • The model considers one ideological dimension and alignment; multidimensional ideologies and claims with varying alignment factors remain for future study.Source: Conclusion · p. 9

Methods

The methods model misinformation through ideological attributes and social influence in a three-state framework, examining its behavior with simulations, Markov analysis, and network configurations. They vary ideological intensity and extremist placement, including degree-based, localized BFS, and random strategies.

Model

  • Agents occupy susceptible, believer, or fact-checker states on a static, undirected network; believers may verify claims, and believers or fact-checkers may forget and return to susceptibility.Verification halts spread and contributes to debunking; forgetting occurs with probability pforget.Source: Model · pp. 10–12
  • When claims align with ideology, greater intensity increases susceptibility to believers; when they conflict, it reduces susceptibility and increases the relative weight of fact-checkers, while neutral claims remove intensity differences.The model represents individual intensity and claim alignment separately, allowing the same person to respond differently to claims with different alignments.Source: Model · pp. 10–11

Simulation

  • Simulations used 98% susceptible, 1% believer, and 1% fact-checker initial states, ran 500 steps and 50 trials per parameter combination, and varied alignment from −1 to 1 and intensity across four values.The reported believer percentage is the trial mean, with uncertainty given by 95% confidence intervals.Source: Simulation · p. 12

Markov Representation

  • Complementary numerical and analytical solutions characterize steady-state belief dynamics; node-level belief probabilities and standardized OLS models assess individual and neighbour ideological intensity across network structures and placement strategies.The study used ER, WS, and BA synthetic networks alongside an empirical Facebook network sample.Source: Markov Representation · p. 12, Statistical Analysis · p. 13, Networks · p. 13

Ideological Intensity and Extremist Definitions

  • Ideological intensity was configured as homogeneous, binary moderate-versus-extremist, or continuously heterogeneous, with extremists defined as the population’s top 30% by intensity.In the heterogeneous case, intensities are sampled uniformly from [0, 1], and extremists’ network positions are independent of intensity unless a placement strategy is specified.Source: Ideological Intensity and Extremist… · pp. 13–14

Extremist Placement Strategies

Appendix

For the homogeneous-intensity case, the appendix derives a mean-field analytical solution for the steady-state fraction of believers. Fig. 9 presents that fraction across ideological alignment and intensity.

Appendix

  • The analysis uses a mean-field treatment for the case where all individuals share the same ideological intensity.It derives the steady-state fraction of believers for this homogeneous-intensity case.Source: Appendix · p. 17
  • At the population level, the transition probabilities f_i(t) and g_i(t) are replaced by expressions f and g.The expressions are given in equation 7.Source: Appendix · p. 17
  • The steady-state derivation rearranges equations 4 and 5 and constrains the state probabilities to sum to 1.Solving these conditions yields P(B), the steady-state fraction of believers.Source: Appendix · p. 18
  • The analytical solution gives the steady-state fraction of believers across ideological alignment and intensity in Fig. 9.The figure presents the outcome of the homogeneous-intensity analysis.Source: Appendix · p. 18, Figure 9 · p. 18
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