Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methods

Interian, Ruben; Marzo, Ruslán G.; Bernardini, Flávia; Ribeiro, Celso C.

doi:10.1111/itor.13224

Cited by 17 publications

(9 citation statements)

References 123 publications

(252 reference statements)

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“…a subgraph that contains all vertices in A i and all edges between vertices in A i ), then the modularity value Q for this single group will be equal to zero [29]. A previous study showed that there is a lack of consolidated group-level measures for evaluating polarization [21].…”

Section: Modularity and Contribution Of Groups To Polarizationmentioning

confidence: 99%

“…They have attracted millions of individuals by allowing them to communicate, share their ideas, and discuss different topics, being an excellent tool for studying individual and group behavior. However, polarization and hostility are increasingly shifting from social media to the real world, as it was demonstrated by several political events, such as the protests of the Yellow Vest movement in France in 2018, the protests after George Floyd's death in 2020, the US Capitol attack in 2021, and the convoy protests in Canada in 2022 [21].…”

Section: Introductionmentioning

confidence: 99%

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Group polarization, influence, and domination in online interaction networks: a case study of the 2022 Brazilian elections

Interian,

Rodrigues

2023

J. Phys. Complex.

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The erosion of social cohesion and polarization is one of the topmost societal risks. In this work, we investigated the evolution of polarization, influence, and domination in online interaction networks using a large Twitter dataset collected before and during the 2022 Brazilian elections. From a theoretical perspective, we develop a methodology called d-modularity that allows discovering the contribution of specific groups to network polarization using the well-known modularity measure. While the overall network modularity (somewhat unexpectedly) decreased, the proposed group-oriented approach reveals that the contribution of the right-leaning community to this modularity increased, remaining very high during the analyzed period. Our methodology is general enough to be used in any situation when the contribution of specific groups to overall network modularity and polarization is needed to investigate. Moreover, using the concept of partial domination, we are able to compare the reach of sets of influential profiles from different groups and their ability to accomplish coordinated communication inside their groups and across segments of the entire network. We show that in the whole network, the left-leaning high-influential information spreaders dominated, reaching a substantial fraction of users with fewer spreaders. However, when comparing domination inside the groups, the results are inverse. Right-leaning spreaders dominate their communities using few nodes, showing as the most capable of accomplishing coordinated communication. The results bring evidence of extreme isolation and the ease of accomplishing coordinated communication that characterized right-leaning communities during the 2022 Brazilian elections, which likely influenced the subsequent coup events in Brasilia.

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Section: Modularity and Contribution Of Groups To Polarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Group polarization, influence, and domination in online interaction networks: a case study of the 2022 Brazilian elections

Interian,

Rodrigues

2023

J. Phys. Complex.

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“…In Matlab environment, this paper simulates the performance of gray coded BOTDA sensor under different noise influences [21]. Firstly, this paper constructs a mathematical model for simulation, which considers the case that the frequency difference between pump light and probe light is 10.684GHz.…”

Section: The Influence Of Polarization Random Noise and Its Suppressi...mentioning

confidence: 99%

Polarization Random Noise Suppression Method in Two-Dimensional Force Sensor Based on Random Forest

2023

IJOMAM

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The technique of distributed optical fiber sensing is very effective and applied in many different industries. Because of its benefits, including a long sensing distance and good spatial resolution, Brillouin Optical Time Domain Analysis (BOTDA) has received a lot of interest. A method for reducing the polarization random noise of a two-dimensional force sensor based on random forest is proposed in order to lessen the restriction between long sensing distance and high spatial resolution and measurement accuracy. The random forest algorithm is presented first. The polarization correlation of Stimulated Brillouin Scattering (SBS) and its impact on Brillouin time domain analysis and sensing are then examined. By studying polarization random noise, a method to suppress polarization random noise based on random forest algorithm and Brillouin phase shift spectrum (BPS) is proposed. Following that, simulation studies are used to confirm the method's impact on enhancing sensing accuracy. Finally, an analysis is done of the impact of gray coded BOTDA system sensors on wavelet-based image threshold denoising technology. The findings demonstrate that while the wavelet threshold denoising method is excellent at denoising white noise, it is not successful at suppressing polarization random noise, which lowers the sensing accuracy. Polarization random noise is less of an issue with BPS than it is with Brillouin gain spectrum (BGS), which allows for a three-fold increase in sensing accuracy. Wavelet denoising has a denoising impact on Brillouin phase shift sensing data that is unquestionably superior to Brillouin gain, and the BFS fluctuation is unquestionably decreased after denoising. This paper offers some suggestions and guidelines for reducing polarization random noise in sensors.

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“…Measures of polarization proposed in the literature depend on the system that is being investigated (Interian et al, 2023). Most analyses focus on political parties and parliaments.…”

Section: Introductionmentioning

confidence: 99%

A general model for how attributes can reduce polarization in social groups

Górski

Atkisson

Hołyst

2023

Net Sci

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Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating attributes of individuals usually makes structural balance, and hence polarization, harder to achieve. That work examines only a limited number and types of attributes. We present a generalized model and a simulation framework to analyze the effect of any type of attribute, including analytically as long as an expected value can be written for the type of attribute. As attributes, we consider people’s (approximately) immutable characteristics (e.g., race, wealth) and such opinions that change more slowly than relationships (e.g., political preferences). We detail and analyze five classes of attributes, recapitulating the results of previous work in this framework and extending it. While it is easier to prevent than to destabilize polarization, we find that usually the most effective at both are continuous attributes, followed by ordered attributes and, finally, binary attributes. The effectiveness of unordered attributes varies depending on the magnitude of negative impact of having differing attributes but is smaller than of continuous ones. Testing the framework on network structures containing communities revealed that destroying polarization may require introducing local tensions. This model could be used by policymakers, among others, to prevent and design effective interventions to counteract polarization.

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Network polarization, filter bubbles, and echo chambers: an annotated review of measures and reduction methods

Cited by 17 publications

References 123 publications

Group polarization, influence, and domination in online interaction networks: a case study of the 2022 Brazilian elections

Group polarization, influence, and domination in online interaction networks: a case study of the 2022 Brazilian elections

Polarization Random Noise Suppression Method in Two-Dimensional Force Sensor Based on Random Forest

A general model for how attributes can reduce polarization in social groups

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