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.

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 An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks

2021 ◽  
Author(s):  
Unchitta Kan ◽  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Social Networks ◽  
Early Time ◽  
Opinion Dynamics ◽  
Convergence Time ◽  
Confidence Bound ◽  
Neighboring Node ◽  
Popular Approach ◽  
Time Dynamics ◽  
Bounded Confidence ◽  
Bounded Confidence Model

Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to studying the dynamics of opinion spread on networks is by examining bounded-confidence (BC) models, in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other opinions if they lie within some confidence bound of their own opinion. We extend the Deffuant--Weisbuch (DW) model, which is a well-known BC model, by studying opinion dynamics that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinion when they interact with a neighboring node and (2) break a connection with a neighbor based on an opinion tolerance threshold and then form a new connection to a node following the principle of homophily. This opinion tolerance threshold acts as a threshold to determine if the opinions of adjacent nodes are sufficiently different to be viewed as discordant. We find that our adaptive BC model requires a larger confidence bound than the standard DW model for the nodes of a network to achieve a consensus. Interestingly, our model includes regions with `pseudo-consensus' steady states, in which there exist two subclusters within an opinion-consensus group that deviate from each other by a small amount. We conduct extensive numerical simulations of our adaptive BC model and examine the importance of early-time dynamics and nodes with initial moderate opinions for achieving consensus. We also examine the effects of coevolution on the convergence time of the dynamics.

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.

Measuring the role of two competing groups of informed agents in opinion formation

SIMULATION ◽  
2018 ◽  
Vol 95 (8) ◽  
pp. 753-766
Author(s):  
Kamal S Selim ◽  
Ahmed E Okasha ◽  
Fatma R Farag
Keyword(s):  
Opinion Dynamics ◽  
The Other ◽  
Social Characteristics ◽  
Opinion Formation ◽  
Agent Based ◽  
Bounded Confidence ◽  
Significant Difference ◽  
The Impact ◽  
Bounded Confidence Model

For politicians, to promote intended messages to different groups of individuals, they could employ strategic individuals called “informed agents.” The aim of this article is to explore and measure the impact of two competing groups of informed agents on opinion dynamics within a society exposed to two extreme opinions. Thus, an agent-based model is developed as an extension to the bounded confidence model by assuming the existence of two groups of informed agents. The impact of these agents with respect to their social characteristics, such as, their size in the society, how tolerant they are, their self-weight and attitudes about others’ opinions is explored. Different assumptions about the initial opinion distributions and their effect are also investigated. Due to the difficulty of observing a real society, social simulation experiments are constructed based on artificial societies.The simulations conducted resulted in some interesting findings. With no dominating group of the two informed agents, the society will be ended up concentrated around a moderate position. On the other hand, with significant difference between the two group sizes, the larger group will polarize the population towards its opinion. However, this conclusion will not apply if the population is skewed towards the other opinion. In such case, the larger group will only succeed to turn some of the society to be more moderate. In a society skewed towards extreme opinion, dominant informed agents adopting the other extreme will not be able to shift the society towards their opinion. Finally, in radical societies informed agents could turn most of the society to be extremists.

Influence of social networks on business processes’ development

2020 ◽  
Vol 19 (12) ◽  
pp. 2225-2252
Author(s):  
E.V. Popov ◽  
V.L. Simonova ◽  
O.V. Komarova ◽  
S.S. Kaigorodova
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Business Models ◽  
Business Processes ◽  
Digital Economy ◽  
Theoretical Understanding ◽  
Sales Channels ◽  
A Value ◽  
The Impact

Subject. The emergence of new ways of interaction between sellers and buyers, the formation of new sales channels and product promotion based on the use of digital economy tools is at the heart of improving the business processes. Social networks became a tool for development; their rapid growth necessitates theoretical understanding and identification of potential application in enterprise's business process digitalization. Objectives. We explore the role of social media in the digitalization of business processes, systematize the impact of social networks on business processes of enterprises in the digital economy. Methods. The theoretical and methodological analysis of social networks as a tool for digitalization of company's business processes rests on the content analysis of domestic and foreign scientific studies, comparison, generalization and systematization. Results. We highlight the key effects of the impact of social networks on the business processes of the company; show that the digitalization of business processes should be considered in the context of a value-based approach, aimed at creating a value through the algorithmization of company operations. We determine that social networks are one of the most important tools for digitalization of company's business processes, as they have a high organizational and management potential. We also systematize the effects of social media on company's business processes. Conclusions. We present theoretical provisions of the impact of social networks on business processes of enterprises, which will enable to model and organize ideas about the development of digital ecosystems and the formation of business models.

Update of the last 2 years on the impact of the internet and social networks on the management of type 2 diabetes mellitus: simple review. (Preprint)

2020 ◽  
Author(s):  
Mayli Lañas-Navarro ◽  
Jose Ipanaque-Calderon Sr ◽  
Fiorela E Solano
Keyword(s):  
Social Networks ◽  
Mobile Applications ◽  
Health Personnel ◽  
Online Courses ◽  
The Internet ◽  
Diabetic Patients ◽  
Bibliographic Search ◽  
Use Of The Internet ◽  
And Control ◽  
The Impact

BACKGROUND Research on the use of the Internet in the medical field is experiencing many advances, including mobile applications, social networks, telemedicine. Its implementation in medical care and comprehensive patient management is a much discussed topic at present. OBJECTIVE This narrative review aims to understand the impact of the internet and social networks on the management of diabetes, both for patients and medical staff. METHODS The bibliographic search was carried out in the databases Pubmed, Virtual Health Library (VHL) and Lilacs between 2018 to 2020. RESULTS Multiple mobile applications have been created for the help and control of diabetic patients, as well as the implementation of online courses, improving the knowledge of health personnel applying them in the field of telemedicine. CONCLUSIONS The use of the Internet and social networks brings many benefits for both the diabetic patient and the health personnel, offering advantages for both.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

Sign in / Sign up

Export Citation Format

Share Document