scholarly journals Brokering Votes with Information Spread via Social Networks

2019 ◽  
Author(s):  
Raul Duarte ◽  
Frederico Finan ◽  
Horacio Larreguy Arbesu ◽  
Laura Schechter
2019 ◽  
Vol 30 (11) ◽  
pp. 1950094 ◽  
Author(s):  
Jianye Yu ◽  
Junjie Lv ◽  
Yuanzhuo Wang ◽  
Jingyuan Li

Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.


2017 ◽  
Vol 20 (4) ◽  
Author(s):  
Kalina Grzesiuk

Companies which decide on socially responsible activities usually take into consideration benefits including the marketing effects of CSR programmes. However, in order to achieve that, the information about the socially responsible activities of companies must be spread and reach the audience of the company. That includes stakeholders related to the company that might be interested in receiving information about the social initiatives undertaken by the company. These stakeholders are connected with the firm through the network of social ties (SN). The main goal of this article is to present a theoretical framework of roles that these networks of social ties play in the effective communication of CSR activities. This paper is divided into three parts. The first one concerns the problem of how to communicate the involvement of a company in social initiatives. The second one contains the description of possible communication processes and strategies. The last one presents the analysis of the social networks perspective and its main characteristics and, in conclusion, it summarizes the main benefits a company can gain by applying the SN concept to CSR communication in the area of attribution and information spread through various channels.


Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1542
Author(s):  
Alon Bartal ◽  
Kathleen M. Jagodnik

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.


Author(s):  
Jennifer M. Larson ◽  
Janet I. Lewis ◽  
Pedro L. Rodriguez

Abstract From public health to political campaigns, numerous attempts to encourage behavior begin with the spread of information. Of course, seeding new information does not guarantee action, especially when it is difficult for receivers to verify this information. We use a novel design that introduced valuable, actionable information in rural Uganda and reveals the intermediate process that led many in the village to hear the information but only some to act on it. We find that the seeded information spread easily through word of mouth via a simple contagion process. However, acting on the information spread less easily; this process relied instead on endogenously created social information that served to vet, verify, and pass judgment. Our results highlight an important wedge between information that a policy intervention can best control and the behavior that ultimately results.


2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.


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