Toward a Formal Model for Group Polarization in Social Networks

2019 ◽  
pp. 419-441
Author(s):  
Mário S. Alvim ◽  
Sophia Knight ◽  
Frank Valencia
Keyword(s):  
Social Networks ◽  
Formal Model ◽  
Group Polarization

Perverse Accountability: A Formal Model of Machine Politics with Evidence from Argentina

2005 ◽  
Vol 99 (3) ◽  
pp. 315-325 ◽  
Author(s):  
SUSAN C. STOKES
Keyword(s):  
Social Networks ◽  
Formal Model ◽  
Strategic Interaction ◽  
Secret Ballot ◽  
Political Machines ◽  
Deep Insertion ◽  
Electoral Support ◽  
Prisoners Dilemma ◽  
Machine Politics ◽  
The Impact

Political machines (or clientelist parties) mobilize electoral support by trading particularistic benefits to voters in exchange for their votes. But if the secret ballot hides voters' actions from the machine, voters are able to renege, accepting benefits and then voting as they choose. To explain how machine politics works, I observe that machines use their deep insertion into voters' social networks to try to circumvent the secret ballot and infer individuals' votes. When parties influence how people vote by threatening to punish them for voting for another party, I call thisaccountability. I analyze the strategic interaction between machines and voters as an iterated prisoners' dilemma game with one-sided uncertainty. The game generates hypotheses about the impact of the machine's capacity to monitor voters, and of voters' incomes and ideological stances, on the effectiveness of machine politics. I test these hypotheses with data from Argentina.

Fuzzy Soft Social Network Modeling and Marketing

2012 ◽  
pp. 16-40 ◽  
Author(s):  
Ronald R. Yager ◽  
Rachel L. Yager
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Fuzzy Set ◽  
Granular Computing ◽  
Formal Model ◽  
Mathematical Representation ◽  
Human Beings ◽  
Linguistic Terms ◽  
Social Network Modeling

Facebook, Linkedin, Myspace, and other social networks have become a very important environment in which people interact, exchange information about products, services, movies and music, and so forth. New trends and hot items rapidly move through these networks. Clearly, modern marketing has to focus on the possibilities of taking advantage of these networks. The determination of people who are leaders and trendsetters within a social network would be a great benefit for marketing. In recent papers, the authors have developed a model of social networks based on the use of fuzzy set theory and other soft granular computing technologies. This is called the Framework for Intelligent Social Network Analysis (FISNA). Using granular computing, the authors express concepts associated with social networks in a human-focused manner. Since human beings predominantly use linguistic terms in order to communicate, reason, and understand, they are able to build bridges between human conceptualization and the formal mathematical representation of the social networks. Consider, for example, a concept such as “leader.” An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, the properties of a leader. The authors’ framework enables translation of this linguistic description into a mathematical formalism that allows for determination of how true it is that a particular person, a node in the network, is a leader. The authors use fuzzy set methodologies, and more generally granular computing, to provide the necessary bridge between the human analyst and the formal model of the network. In this chapter, the authors investigate and describe the use of the FISNA technology to help in the modeling of market related concepts in social networks.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

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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