scholarly journals Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 265
Author(s):  
Tyson Pond ◽  
Saranzaya Magsarjav ◽  
Tobin South ◽  
Lewis Mitchell ◽  
James P. Bagrow
Keyword(s):  
Information Flow ◽  
Social Reinforcement ◽  
Real World Data ◽  
Information Theoretic ◽  
Multiple Exposures ◽  
Written Information ◽  
The Social ◽  
Social Flow ◽  
Network Properties ◽  
Complex Features

Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

scholarly journals Social Media Versus Personal Experience in the Consumer’s Apprehension on Imported Food Security and Safety Dilemma

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110074
Author(s):  
Tariq H. Malik ◽  
Jae Chul Choi
Keyword(s):  
Social Media ◽  
Food Security ◽  
Food Safety ◽  
Information Flow ◽  
Free Market ◽  
Direct Experience ◽  
Information Quantity ◽  
The Social ◽  
Imported Food ◽  
Aquatic Food

South Korea imports a large amount of agricultural and aquatic food products from China, which meets its food security. However, the import from China raises food safety questions, leading to food safety apprehension. We explored the source of the Korean consumer’s apprehension. Based on the apprehension reduction theory (ART) developed from interviews with Korean consumers in the first stage of the study, we conducted a survey to assess the social media as an indirect source of information and direct experience of the consumer in the second stage of the study. We received 504 responses, of which 1/3 of the respondents had visited China in the last year. Using FSS (Food Safety Satisfaction) as the dependent variable (1— low to 5— high), we link information from the social media vis-à-vis direct experience and made three discoveries. (a) The information quantity of social media increases the consumer’s apprehension, partially refuting the ART. (ii) FSS increased in response to information flow from the direct experience of the consumer with Chinese imported food. (c) The direct information from experience mediates the effects of indirect information (social media) on apprehension about agricultural and aquatic product imports. We made three inferences. First, information quantity and quality have separated roles in the ART. Second, social media increases the free-market style information flow, turning legitimate products to illegitimate and vice versa. Third, the collective irrationality from the information quantity needs institutional bricolage to legitimize the chaotic nature of the untamed information.

Social Simulation and its Process in Learning

2014 ◽  
pp. 355-366
Author(s):  
Bo Chang
Keyword(s):  
Social Context ◽  
Social Simulation ◽  
Social Activities ◽  
Healthcare Education ◽  
Future Trends ◽  
Linear Features ◽  
The Social ◽  
Real Objects ◽  
Computerized Simulation ◽  
Complex Features

Simulation has been applied in the fields of computers, engineering, entertainment, healthcare, education, training, etc. Much research on simulation uses computerized programs to imitate real objects or to visualize hypothesized objects. Due to the complex features of social societies, and the non-linear features of knowledge in the social context, it is somewhat rigid for learners to use computerized simulation programs to understand social activities. Therefore, the purpose of this chapter is to discuss simulation in the social context. The author first introduces the background of simulation. Then she discusses non-computerized social simulation and the process of how to apply social simulation in practice. Finally, the author points out the future trends of simulation.

Information Diffusion in Social Networks

2011 ◽  
pp. 146-163 ◽  
Author(s):  
Dmitry Zinoviev
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Diffusion Processes ◽  
Small World ◽  
The Social ◽  
Strength Of Weak Ties ◽  
Strategic Position

The issue of information diffusion in small-world social networks was first systematically brought to light by Mark Granovetter in his seminal paper “The Strength of Weak Ties” in 1973 and has been an area of active academic studies in the past three decades. This chapter discusses information proliferation mechanisms in massive online social networks (MOSN). In particular, the following aspects of information diffusion processes are addressed: the role and the strategic position of influential spreaders of information; the pathways in the social networks that serve as conduits for communication and information flow; mathematical models describing proliferation processes; short-term and long-term dynamics of information diffusion, and secrecy of information diffusion.

scholarly journals Solving design management problems using lean design management: the role of trust

2019 ◽  
Vol 26 (7) ◽  
pp. 1387-1405 ◽  
Author(s):  
Petteri Uusitalo ◽  
Olli Seppänen ◽  
Antti Peltokorpi ◽  
Hylton Olivieri
Keyword(s):  
Conceptual Framework ◽  
Information Flow ◽  
Interpersonal Trust ◽  
Design Management ◽  
Structured Interviews ◽  
Social Domain ◽  
Content Type ◽  
The Social ◽  
Management Problems

Purpose Although prior studies have noted the importance of trust for project performance, research remains scant on describing the role of trust when using lean design management (LDM) in projects. The purpose of this paper is to explore the connection between LDM and interpersonal trust in solving construction projects’ design management problems. Design/methodology/approach A qualitative study was conducted that included 29 trust- and LDM-themed semi-structured interviews in the USA (California), Brazil and Finland; 11 focus group discussions were also organized to validate the interview findings. Findings The study reveals how LDM contributes to solving design management problems through two distinct but interconnected mechanisms: improved information flow; and improved trust among project team members. A conceptual framework was crafted to illustrate the mechanisms in building trust by means of the social domain of LDM concepts. Research limitations/implications The conceptual framework requires testing through an international survey or through multiple case studies. Practical implications The results indicate that design management would benefit from trustful environments and that trust may be the catalyst for actors’ engagement with LDM. Managers in charge of design within projects can use the conceptual framework when selecting the appropriate LDM tools, which should include both the social and technical domains. Originality/value The study emphasizes the importance of the social domain of LDM concepts. Previous studies have focussed on information flow aspects of LDM but have overlooked the value of interpersonal trust in solving design management problems.

scholarly journals Modelling community formation driven by the status of individual in a society

2017 ◽  
Vol 5 (6) ◽  
pp. 817-838 ◽  
Author(s):  
Jan E Snellman ◽  
Gerardo Iñiguez ◽  
Tzipe Govezensky ◽  
R A Barrio ◽  
Kimmo K Kaski
Keyword(s):  
Social Status ◽  
Network Size ◽  
Community Formation ◽  
Human Society ◽  
Agent Based ◽  
The Social ◽  
The Status ◽  
Network Properties ◽  
Willingness To Compete ◽  
New Strategies

Abstract In human societies, people’s willingness to compete and strive for better social status, as well as being envious of those perceived in some way superior, lead to social structures that are intrinsically hierarchical. Here, we propose an agent-based, network model to mimic the ranking behaviour of individuals and its possible repercussions in human society. The main ingredient of the model is the assumption that the relevant feature of social interactions is each individual’s keenness to maximize his or her status relative to others. The social networks produced by the model are homophilous and assortative, as frequently observed in human communities, and most of the network properties seem quite independent of its size. However, we see that for a small number of agents the resulting network consists of disjoint weakly connected communities, while being highly assortative and homophilic. On the other hand, larger networks turn out to be more cohesive with larger communities but less homophilic. We find that the reason for these changes is that larger network size allows agents to use new strategies for maximizing their social status, allowing for more diverse links between them.

scholarly journals Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

2019 ◽  
Vol 11 (4) ◽  
pp. 95
Author(s):  
Wang ◽  
Zhu ◽  
Liu ◽  
Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Memory Effect ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Social Reinforcement ◽  
Reinforcement Effect ◽  
The Social ◽  
Influence Maximization Problem

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

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