scholarly journals Opinion formation in time-varying social networks: The case of the naming game

2012 ◽  
Vol 86 (3) ◽  
Author(s):  
Suman Kalyan Maity ◽  
T. Venkat Manoj ◽  
Animesh Mukherjee
Keyword(s):  
Social Networks ◽  
Time Varying ◽  
Opinion Formation ◽  
Naming Game

scholarly journals Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021 ◽  
Vol 13 (3) ◽  
pp. 76
Author(s):  
Quintino Francesco Lotito ◽  
Davide Zanella ◽  
Paolo Casari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Simulation Models ◽  
Epidemic Models ◽  
Time Varying ◽  
Fake News ◽  
Network Access ◽  
Spreading Process ◽  
Network Topologies ◽  
Access Patterns

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

PHYSICS OF THE MIND: OPINION DYNAMICS AND DECISION MAKING PROCESSES BASED ON A BINARY NETWORK MODEL

2008 ◽  
Vol 22 (25n26) ◽  
pp. 4482-4494 ◽  
Author(s):  
F. V. KUSMARTSEV ◽  
KARL E. KÜRTEN
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Chaotic Behavior ◽  
Opinion Dynamics ◽  
Human Mind ◽  
Critical Parameters ◽  
Opinion Formation ◽  
Natural Catastrophes ◽  
Risk Condition ◽  
Binary Network

We propose a new theory of the human mind. The formation of human mind is considered as a collective process of the mutual interaction of people via exchange of opinions and formation of collective decisions. We investigate the associated dynamical processes of the decision making when people are put in different conditions including risk situations in natural catastrophes when the decision must be made very fast or at national elections. We also investigate conditions at which the fast formation of opinion is arising as a result of open discussions or public vote. Under a risk condition the system is very close to chaos and therefore the opinion formation is related to the order disorder transition. We study dramatic changes which may happen with societies which in physical terms may be considered as phase transitions from ordered to chaotic behavior. Our results are applicable to changes which are arising in various social networks as well as in opinion formation arising as a result of open discussions. One focus of this study is the determination of critical parameters, which influence a formation of stable mind, public opinion and where the society is placed “at the edge of chaos”. We show that social networks have both, the necessary stability and the potential for evolutionary improvements or self-destruction. We also show that the time needed for a discussion to take a proper decision depends crucially on the nature of the interactions between the entities as well as on the topology of the social networks.

scholarly journals Self-Presentation and Adolescent Altruistic Behaviors in Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yaling Zhu ◽  
Yue Shen ◽  
Qiang Zhao
Keyword(s):  
Social Networks ◽  
Opinion Formation ◽  
Self Presentation ◽  
Positive Prediction ◽  
Network Support ◽  
Social Ability ◽  
Spreading Dynamics ◽  
Intermediary Role ◽  
The Impact

Social networks provide a convenient place for people to interact; members in social networks may create new connections or break existing connections, driving the evolution of complex network structure. Dynamics in social networks, such as opinion formation and spreading dynamics, may result in complex collective phenomena. This paper conducts a survey on 495 students from six schools in Shaanxi, Henan, and Zhejiang provinces and discusses the impact of self-presentation on adolescent network altruistic behaviors, the intermediary role of social ability cognition, and the moderating role of privacy awareness. The results show the following: (1) Self-presentation in social networks can positively predict adolescent network altruistic behaviors. The positive prediction effect of network sharing is the largest, and the positive prediction effect of network support is the least. (2) Social ability cognition plays an intermediary role between self-presentation and adolescent network altruistic behaviors. (3) The moderating effect of privacy awareness is not significant.

scholarly journals Dynamics of opinion formation under majority rules on complex social networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vu Xuan Nguyen ◽  
Gaoxi Xiao ◽  
Xin-Jian Xu ◽  
Qingchu Wu ◽  
Cheng-Yi Xia
Keyword(s):  
Social Networks ◽  
Opinion Formation ◽  
Majority Rules

scholarly journals PageRank model of opinion formation on social networks

2012 ◽  
Vol 391 (22) ◽  
pp. 5779-5793 ◽  
Author(s):  
Vivek Kandiah ◽  
Dima L. Shepelyansky
Keyword(s):  
Social Networks ◽  
Opinion Formation

scholarly journals Credit Behaviors of Rural Households in the Perspective of Complex Social Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qiang Zhao ◽  
Yue Shen ◽  
Chaoqian Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Human Behavior ◽  
Positive Impact ◽  
Rural Household ◽  
Opinion Formation ◽  
Informal Credit ◽  
Rural Credit ◽  
Social Network Dynamics ◽  
Household Credit

With the increasing number of social networks emerging and evolving, the influence of social networks on human behavior is now again a subject of discussion in academe. Dynamics in social networks, such as opinion formation and information sharing, are restricting or proliferating members’ behavior on social networks, while new social network dynamics are created by interpersonal contacts and interactions. Based on this and against the backdrop of unfavourable rural credit development, this article uses CHFS data to discuss the whole and heterogeneous impact of social networks on rural household credit behavior. The results show that (1) social networks can effectively promote rural household credit behavior; (2) social networks have a significant positive impact on both formal credit and informal credit, but the influence of the latter is stronger; (3) both emotional networks and instrumental networks have a positive impact on formal credit and informal credit, and their influences are stronger on informal credit; (4) the influence of emotional network is stronger than instrumental networks on either formal credit or informal credit.

scholarly journals A Social Interaction Activity based Time-Varying User Vectorization Method for Online Social Networks

2018 ◽  
Author(s):  
Tianyi Hao ◽  
Longbo Huang
Keyword(s):  
Social Networks ◽  
Social Interaction ◽  
Online Social Networks ◽  
User Modeling ◽  
State Of The Art ◽  
Time Varying ◽  
Self Similarity ◽  
Pairwise Interactions ◽  
User Data ◽  
Model Training

In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features. Tuv is designed based on a novel combination of word2vec, negative sampling and a smoothing technique for model training. It jointly handles multi-format user data and computes user representing vectors, by taking into consideration user feature variation, self-similarity and pairwise interactions among users. The framework enables us to extract hidden user properties and to produce user vectors. We conduct extensive experiments based on a real-world dataset, which show that Tuv significantly outperforms several state-of-the-art user vectorization methods.

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