Time-Varying Truth Prediction in Social Networks Using Online Learning

2020 ◽  
Author(s):  
Olusola T. Odeyomi ◽  
Hyuck M. Kwon ◽  
David A. Murrell
Keyword(s):  
Social Networks ◽  
Online Learning ◽  
Time Varying

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.

Online Learning Communities

2014 ◽  
pp. 107-128 ◽  
Author(s):  
Xavier Inghilterra ◽  
William Samuel Ravatua-Smith
Keyword(s):  
Social Networks ◽  
Online Learning ◽  
Distance Learning ◽  
Learning Communities ◽  
Learning Process ◽  
Peer To Peer ◽  
Online Learning Communities ◽  
Digital Ecosystem ◽  
Successful Approach ◽  
Mediation System

This chapter highlights the potential of educational microblogging as a mediation system to support the process of distance learning. In their experimental approach, the authors conducted participant observations with university students who used their pedagogical device over the course of two semesters. Students participated through peer-to-peer and peer-to-peer to tutor interactions that took place within the academic and personal spheres. In the research corpus, the communitarian dynamic of social networks combined with playful immersion is a fruitful heuristic for individualizing learning paths and strengthening student dedication and commitment. The digital ethnographic participant observations revealed that the sharing and dissemination of information via microblogging allowed the creation of new collaborative methods and development of a culture of participation within the community of student learners. The use of sociotechnical devices such as Twitter and microblogging have proven to be excellent tools for accustoming students to Web 2.0 technologies and ensuring optimal participation in the learning process. This chapter unveils a successful approach to constructing a digital ecosystem where social interactions are initiated (during real-time synchronous educational sessions) and extended outside of academic boundaries into the private sphere. The sociotechnical mediation that the authors have created around Twitter has proven to be very effective in linking these two spatio-temporally contiguous entities for the benefit of learning communities.

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 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.

Sign in / Sign up

Export Citation Format

Share Document