scholarly journals The impact of noise and topology on opinion dynamics in social networks

2021 ◽  
Vol 8 (4) ◽  
Author(s):  
Samuel Stern ◽  
Giacomo Livan
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Peer Influence ◽  
Linear Models ◽  
Explanatory Power ◽  
Opinion Dynamics ◽  
And Topology ◽  
Stochastic Setting ◽  
The Impact ◽  
Do So

We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents’ desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network’s topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.

scholarly journals Synchronising institutions and value systems: a model of opinion dynamics mediated by proportional representation

2021 ◽  
Author(s):  
José Segovia-Martín ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations' information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

scholarly journals Synchronising institutions and value systems: A model of opinion dynamics mediated by proportional representation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257525
Author(s):  
Jose Segovia-Martin ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations’ information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

The power of culture: the gendered impact of family structures and living arrangements on social networks of Chinese older adults

2020 ◽  
pp. 1-24
Author(s):  
Menghan Zhao ◽  
Fan Yang ◽  
Youlang Zhang
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Rural Women ◽  
Living Arrangements ◽  
Filial Piety ◽  
Linear Models ◽  
Aged Care ◽  
Chinese Society ◽  
Family Structures ◽  
The Impact

Abstract Most of the extant literature on the fertility history and social networks of older adults has focused on advanced societies. Nevertheless, a limited number of studies have explored how culturally preferred family structures or living arrangements are related to older adults’ social networks in developing societies. This study examined these issues in the Chinese context and paid particular attention to the filial piety and preference for sons dominating Chinese society. Using nationally representative data of adults aged 60 and over from China Longitudinal Aging Social Survey in 2016, we constructed family and friend network scores following previous studies and developed linear models using multiple imputation for the missing data. The results suggested that childless older adults were the most disadvantaged in receiving support from family networks. Despite China's patrilineal culture, daughters were important sources of support. In terms of friend networks, older men who had no sons were least likely to receive support while co-residing with a partner and a son(s) might benefit them. Further analysis revealed that older rural women, but not older urban women, also had more support from friend networks if living with sons, implying urban–rural differences. Given the impact of social networks on older adults’ health and wellbeing, older Chinese people with no sons might need more support from other sources, such as aged-care programmes from public institutions, to achieve healthy ageing.

scholarly journals COVID-19 Morbidity and Mortality in Social Networks: Does It Influence Vaccine Hesitancy?

2021 ◽  
Vol 18 (18) ◽  
pp. 9448
Author(s):  
Jagdish Khubchandani ◽  
Sushil Sharma ◽  
James H. Price ◽  
Michael J. Wiblishauser ◽  
Fern J. Webb
Keyword(s):  
Social Networks ◽  
Random Sample ◽  
Family Members ◽  
Morbidity And Mortality ◽  
Vaccine Hesitancy ◽  
The Impact ◽  
Vaccination Hesitancy ◽  
Do So

The impact of COVID-19 morbidity and mortality among family and friends on vaccination preferences is not well explored. A valid and reliable questionnaire was deployed online via mTurk to recruit a national random sample of adult Americans to understand COVID-19 vaccination preferences and its relationship with COVID-19 infection in social networks. A total of 1602 individuals participated in the study where the majority had taken at least one dose of the COVID-19 vaccine (79%) and almost a tenth were planning to do so (10%) or did not want to take the vaccine (11%). Compared to those who knew family members or friends affected by COVID-19, those who did not know anyone infected with (AOR = 3.20), hospitalized for (AOR = 3.60), or died of COVID-19 (AOR = 2.97) had statistically significantly higher odds of refusing the vaccines. Most strategies for reducing COVID-19 vaccination hesitancy focus on highlighting the benefits of COVID-19 vaccines. We suggest that the dangers of not getting the vaccine should also be emphasized as many people who do not know someone who was affected with COVID-19 are also hesitant towards vaccination. These individuals may not fully appreciate the morbidity and mortality impact of COVID-19 infections and the messaging can be tailored to highlight the risk of not having vaccines.

scholarly journals Detecting Bots and Assessing Their Impact in Social Networks

2021 ◽  
Author(s):  
Nicolas Guenon des Mesnards ◽  
David Scott Hunter ◽  
Zakaria el Hjouji ◽  
Tauhid Zaman
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Physics ◽  
Opinion Dynamics ◽  
Data Sets ◽  
Social Network Data ◽  
Operational Capabilities ◽  
Dynamics Models ◽  
The Impact ◽  
The U.S

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.

scholarly journals Evolution of Bounded Confidence Opinion in Social Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Hui Xie ◽  
Guangjian Li ◽  
Yongjie Yan ◽  
Sihui Shu
Keyword(s):  
Social Networks ◽  
Long Range ◽  
Large Range ◽  
Dynamical Behavior ◽  
Opinion Dynamics ◽  
Bounded Confidence ◽  
Shannon Information Entropy ◽  
Experimental Findings ◽  
The Impact ◽  
Full Consensus

We investigate opinion dynamics as a stochastic process in social networks. We introduce the stubborn agent in order to determine the impact of network structure on the emergence of consensus. Depending on the fraction of undirected long-range connections, we observe fascinatingly rich dynamical behavior and transitions from disordered to ordered states. In general, we find that the stubborn agent promotes the emergence of consensus due to the so-called “group effect” that facilitates coalescence between separated network components. Agents are also behaviorally constrained Shannon information entropy in networks. However, since agents want to evolve their opinion with Brownian motion, which may in turn impede full consensus, sufficiently frequent long-range links are in such situations crucial for the network to converge into an absorbing phase. Our experimental findings indicate that, for a large range of control parameters, our model yields stable and fluctuating polarized states.

Explaining and predicting the impact of authors within a community: an assessment of the bibliometric literature and application of machine learning

2019 ◽  
Vol 29 (1) ◽  
pp. 61-80
Author(s):  
Sen Chai ◽  
Alexander D’Amour ◽  
Lee Fleming
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Explanatory Power ◽  
Large Body ◽  
Explanatory Models ◽  
Support Vector ◽  
One Year ◽  
Number Of Publications ◽  
The Impact ◽  
Highly Cited

Abstract Following widespread availability of computerized databases, much research has correlated bibliometric measures from papers or patents to subsequent success, typically measured as the number of publications or citations. Building on this large body of work, we ask the following questions: given available bibliometric information in one year, along with the combined theories on sources of creative breakthroughs from the literatures on creativity and innovation, how accurately can we explain the impact of authors in a given research community in the following year? In particular, who is most likely to publish, publish highly cited work, and even publish a highly cited outlier? And, how accurately can these existing theories predict breakthroughs using only contemporaneous data? After reviewing and synthesizing (often competing) theories from the literatures, we simultaneously model the collective hypotheses based on available data in the year before RNA interference was discovered. We operationalize author impact using publication count, forward citations, and the more stringent definition of being in the top decile of the citation distribution. Explanatory power of current theories altogether ranges from less than 9% for being top cited to 24% for productivity. Machine learning (ML) methods yield similar findings as the explanatory linear models, and tangible improvement only for non-linear Support Vector Machine models. We also perform predictions using only existing data until 1997, and find lower predictability than using explanatory models. We conclude with an agenda for future progress in the bibliometric study of creativity and look forward to ML research that can explain its models.

scholarly journals Active audiences: Social audience participation in television

Comunicar ◽  
2014 ◽  
Vol 22 (43) ◽  
pp. 83-90 ◽  
Author(s):  
Natalia Quintas-Froufe ◽  
Ana González-Neira
Keyword(s):  
Social Networks ◽  
Audience Participation ◽  
Coding System ◽  
Redes Sociales ◽  
Communication Paradigm ◽  
The Social ◽  
La Red ◽  
The Impact ◽  
Traditional Roles ◽  
Do So

The combination of social networks, second screens and TV has given rise to a new relationship between viewers and their televisions, and the traditional roles in the communication paradigm have been altered irrevocably. Social television has spawned the social audience, a fragmentation of the real audience based on how they interact with social networks. This study is an attempt to analyze the factors which contribute to the success or failure of programs with a similar format in relation to their social audience. To do so, the study took as its subject three talent shows launched on the principal mainstream TV channels in Spain in September 2013. The study looked at the impact of these shows on the Twitter network, employing a control form [and developing a categorization and coding system for the analysis with the aim of collating all the data collected]. The results showed that the success of the shows was influenced by the activity in the social network accounts of the presenters and the judges. The conclusions reached in this analysis of the Spanish audience could be used as a development model for social audiences in other countries where social television is not so widespread. La combinación de redes sociales, segundas pantallas y televisión ha propiciado la aparición de una nueva relación de los espectadores con la televisión en la que los habituales roles del paradigma de la comunicación se han alterado. La televisión social ha dado pie al nacimiento de la audiencia social entendida como una fragmentación de la audiencia real en función de su interactividad en las redes sociales. Este trabajo pretende estudiar los elementos que contribuyen al éxito o fracaso de programas con un mismo formato en relación a la audiencia social. Para ello se han tomado como objeto de estudio los tres talent show que lanzaron las principales cadenas generalistas españolas en septiembre del año 2013. Se ha procedido a la observación del impacto de dichos programas en la red social Twitter empleando una ficha de elaboración propia y se desarrolló un sistema de categorías de análisis y códigos con el fin de recopilar toda la información recogida. Los resultados obtenidos indican que en el éxito de los programas analizados en audiencia social influye la actividad de la cuentas de los presentadores y del jurado. Las conclusiones alcanzadas tras este análisis de la experiencia española pueden servir como modelo de desarrollo de la audiencia social para otros países en los que esta no se encuentre tan extendida.

Persuasion process on social networks

2021 ◽  
Author(s):  
Haibo Hu ◽  
Nannan Xu
Keyword(s):  
Social Networks ◽  
Mass Media ◽  
Social Interactions ◽  
Relative Proportion ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Vital Role ◽  
Opinion Formation ◽  
Media Effect ◽  
The Arab Spring

From the Rwandan genocide to the Arab Spring movement, it has been well known that social networks, offline or online, and mass media can collectively change and amplify public opinions, however there are few theoretical models to characterize the persuasion process. In this paper, we propose an opinion dynamics model based on invasion process with media effect and committed agents, and analytically obtain the fraction of each opinion at the steady state. We find that the relative proportion of committed agents plays a vital role in influencing corresponding opinion formation, and social networks can enhance the influence of committed agents through the interaction between individuals. Mass media can affect individuals not only directly due to exposure but indirectly due to social interactions. This paper reveals the influence of mass media and committed agents on the final distribution of opinions through a persuasion process, and lays the foundation for building more general models that consider individual heterogeneity and external influences.

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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