A model of opinion and propagation structure polarization in social media

Prasetya, Hafizh Adi; Murata, Tsuyoshi

doi:10.1186/s40649-019-0076-z

Cited by 36 publications

(32 citation statements)

References 70 publications

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“…A common measure of polarization, sometimes referred to as "average edge homogeneity" [36,72], is the difference between the fraction of ILs and CLs, here denoted by 1]. When m = 0, Λ follows homophily closely with Λ = 0,±1 when J = 0,±1, respectively.…”

Section: Model Formulation and General Propertiesmentioning

confidence: 99%

“…In order to characterize the level of partial division between the parties arising for intermediate homophily, −1 < J < 1 (Fig. 1(c,d)), polarization is often measured in terms of the so-called the average edge homogeneity [36,72]. The latter quantity, here denoted by Λ is defined as the difference between the fraction of ILs and CLs, that is Λ = 1 − 2ρ.…”

Section: Polarizationmentioning

confidence: 99%

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How does homophily shape the topology of a dynamic network?

Mobilia

Rucklidge

et al. 2021

Phys. Rev. E

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We consider a dynamic network of individuals that may hold one of two different opinions in a two-party society. As a dynamical model, agents can endlessly create and delete links to satisfy a preferred degree, and the network is shaped by homophily, a form of social interaction. Characterized by the parameter J ∈ [−1,1], the latter plays a role similar to Ising spins: agents create links to others of the same opinion with probability (1 + J)/2, and delete them with probability (1 − J)/2. Using Monte Carlo simulations and mean field theory, we focus on the network structure in the steady state. We study the effects of J on degree distributions and the fraction of cross-party links. While the extreme cases of homophily or heterophily (J = ±1) are easily understood to result in complete polarization or anti-polarization, intermediate values of J lead to interesting features of the network. Our model exhibits the intriguing feature of an "overwhelming transition" occurring when communities of different sizes are subject to sufficient heterophily: agents of the minority group are oversubscribed and their average degree greatly exceeds that of the majority group. In addition, we introduce a novel measure of polarization which displays distinct advantages over the commonly used average edge homogeneity.

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Section: Model Formulation and General Propertiesmentioning

confidence: 99%

Section: Polarizationmentioning

confidence: 99%

How does homophily shape the topology of a dynamic network?

Mobilia

Rucklidge

et al. 2021

Phys. Rev. E

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“…In this context, three large and barely-polarized clusters come to light. On the one hand, cluster 3 (≈ 10K members) seems to validate the claim that "structure segregation and opinion polarization share no apparent causal relationship" (Prasetya and Murata 2020). It includes left-wing opponents to the referendum as well as several media accounts and has very low polarization (-0.04), a probable evidence of the willingness of the left-wing members of the NO alignment to maintain a cross-partisan interaction with the democrats.…”

Section: Clustering Structurementioning

confidence: 66%

Characterizing networks of propaganda on twitter: a case study

et al. 2020

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The daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the "polarization" of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a "central" role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.

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“…Our results confirm the capacity for dissonance between positions on intervention in general and for specific interventions, as hypothesized in previous AUSPOPS research [ 10 ]. More broadly, research on the question of whether social opinion is becoming more polarized due to the reinforcing nature of personalized news and information, especially through social media, has shown mixed results [ 72 – 74 ]. While we cannot test this assumption with our data, future research may benefit from gauging the extent of respondents’ use of social media and other information sources to investigate potential relationship between engagement with more personalized news streams and strength of opinion.…”

Section: Discussionmentioning

confidence: 99%

Changes in Australian community perceptions of non-communicable disease prevention: a greater role for government?

et al. 2021

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Background Monitoring trends in community opinion can identify critical opportunities to implement upstream health policies or interventions. Our study examines change and demographic modifiers of change in community perceptions of government intervention for prevention of lifestyle-related chronic disease across two time points in Australia. Methods Data were drawn from the 2016 (n = 2052) and 2018 (n = 2601) waves of a nationally representative cross-sectional telephone survey, ‘AUSPOPS’. Survey questions gauged perceptions of government intervention for health in general, peoples’/organizations’ role in maintaining health (e.g., parents, government) and support for specific health interventions (e.g., taxing soft drink). Bivariate and multivariate regression models tested for change between the two surveys, adjusted for demographic characteristics. Models with interactions between survey wave and demographic variables tested for differential change. One-tailed variance ratio tests examined whether opinions had become more polarized in 2018 compared with 2016. Results The large, significant increase observed in the perceived size of the role that government has in maintaining people’s health was uniform across demographic subpopulations. The role for employers and private health insurers was also perceived to be larger in 2018 compared with 2016, but the degree of change varied by gender, age and/or socioeconomic status. Support for some government interventions (e.g., taxing soft drinks) increased among specific demographic subgroups whilst exhibiting no overall change. Opinion was more polarized on general attitudes to government intervention for population health in 2018 compared to 2016, despite little change in central tendency. Conclusions Opportunities may exist to implement government health-promoting policies (e.g., taxing soft drinks), although advocacy may be needed to address the concerns of less supportive subpopulations. Attitudes on government intervention in general may be becoming more polarized; future research examining the association of such changes with exposure to different information sources could inform communication strategies for future health policy change.

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A model of opinion and propagation structure polarization in social media

Cited by 36 publications

References 70 publications

How does homophily shape the topology of a dynamic network?

How does homophily shape the topology of a dynamic network?

Characterizing networks of propaganda on twitter: a case study

Changes in Australian community perceptions of non-communicable disease prevention: a greater role for government?

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