scholarly journals Social Learning in Social Networks

2009 ◽  
Author(s):  
P. J. Lamberson
Keyword(s):  
Social Networks ◽  
Social Learning

Social Learning Analytics

2021 ◽  
pp. 190-206
Author(s):  
Mustafa Şahin Bülbül
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Learning Analytics ◽  
Social Learning Analytics ◽  
Shed Light

Despite the idea that learning is individual, the YouTube channel has been examined to explain how learning is controlled in social networks. What kind of mechanism does the YouTube channel, which deeply influences the education world, work with and what does this structure tell the educators? What do data such as the number of views and comments on YouTube mean? Also, what kind of a model can be established between the video proposition system and our individual and social learning? This study has been prepared to shed light on the questions mentioned.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

Learning in Social Networks

2016 ◽  
pp. 503-542 ◽  
Author(s):  
Benjamin Golub ◽  
Evan Sadler
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Learning Theories ◽  
Social Connections ◽  
Network Centrality ◽  
Learning Models ◽  
Network Learning ◽  
Aggregation Of Information

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. The chapter begins with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, the chapter presents the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, the chapter turns to various models of repeated updating that feature richer optimizing behavior, and concludes by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

scholarly journals Power Indices in the Context of Social Learning Behaviour in Social Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ruili Shi ◽  
Chunxiang Guo ◽  
Xin Gu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Learning ◽  
Social Impact ◽  
Power Index ◽  
Decision Makers ◽  
Power Indices ◽  
Learning Efficiency ◽  
Relationship Closeness ◽  
The Social

This paper puts forward the concept of integrated power, synthetically measures the voters’ ability to influence the results of decision-making by influencing others through social learning, considering the interactions between decision-makers in social networks, and offers a method for measuring integrated power. Based on the theory and model of social learning, we analyze the influence of social learning on the voting process and power indices from the perspective of individuals’ professional level, position within the social network structure, relationship closeness, and learning efficiency. A measurement model of integrated power is constructed, and the variation in integrated power compared with that of the Banzhaf index is analyzed by numerical simulation. The results show that when the individual’s professional level is higher and closeness with neighboring decision-makers is greater, then the integrated power index is higher. An individual’s integrated power index may decrease when he/she changes from an isolated node to a nonisolated node, and then his/her integrated power will increase with the increases of neighbor nodes. Social learning efficiency can promote the integrated power of individuals with lower social impact and relationship closeness, but it is not beneficial for the core and influential members of the social network.

scholarly journals How social learning amplifies moral outrage expression in online social networks

2021 ◽  
Author(s):  
William J. Brady ◽  
Killian Lorcan McLoughlin ◽  
Tuan Nguyen Doan ◽  
Molly Crockett
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Online Social Networks ◽  
Social Life ◽  
Learning Processes ◽  
Moral Outrage ◽  
Social Feedback ◽  
Learning Mechanisms ◽  
Behavioral Experiments ◽  
Social Learning Processes

Moral outrage shapes fundamental aspects of human social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two pre-registered observational studies of Twitter (7,331 users and 12.7 million total tweets) and two pre-registered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. We also find that outrage expressions are sensitive to expressive norms in users’ social networks, over and above users’ own preferences, suggesting that norm learning processes guide online outrage expressions. Moreover, expressive norms moderate social reinforcement of outrage: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to impact moral discourse in digital public spaces.

Understanding social networks requires more than two dimensions

2014 ◽  
Vol 37 (1) ◽  
pp. 99-99
Author(s):  
Derek Ruths ◽  
Thomas Shultz
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Learning ◽  
Social Influence ◽  
Two Dimensions ◽  
Current Evidence ◽  
Learning Techniques

AbstractThe proposed framework is insufficient to categorize and understand current evidence on decision making. There are some ambiguities in the questions asked that require additional distinctions between correctness and accuracy, decision making and learning, accuracy and confidence, and social influence and empowerment. Social learning techniques are not all the same: Behavior copying is quite different from theory passing. Sigmoidal acquisition curves are not unique to social learning and are often mistaken for other accelerating curves.

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