scholarly journals Misinformation, Radicalization and Hate Through the Lens of Users

2020 ◽  
Author(s):  
Manoel Horta Ribeiro ◽  
Virgílio A. F. Almeida ◽  
Wagner Meira Jr
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Network Structure ◽  
Online Social Networks ◽  
Hate Speech ◽  
Far Right ◽  
Content Creation ◽  
Political Opinion

The popularization of Online Social Networks has changed the dynamics of content creation and consumption. In this setting, society has witnessed an amplification in phenomena such as misinformation and hate speech. This dissertation studies these issues through the lens of users. In three case studies in social networks, we: (i) provide insight on how the perception of what is misinformation is altered by political opinion; (ii) propose a methodology to study hate speech on a user-level, showing that the network structure of users can improve the detection of the phenomenon; (iii) characterize user radicalization in far-right channels on YouTube through time, showing a growing migration towards the consumption of extreme content in the platform.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

Modelling multi-topic information propagation in online social networks based on resource competition

2016 ◽  
Vol 43 (3) ◽  
pp. 342-355 ◽  
Author(s):  
Liyuan Sun ◽  
Yadong Zhou ◽  
Xiaohong Guan
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Resource Competition ◽  
Propagation Model ◽  
Small Scale ◽  
Model Parameters ◽  
Information Propagation ◽  
Practical Applications ◽  
The Impact

Understanding information propagation in online social networks is important in many practical applications and is of great interest to many researchers. The challenge with the existing propagation models lies in the requirement of complete network structure, topic-dependent model parameters and topic isolated spread assumption, etc. In this paper, we study the characteristics of multi-topic information propagation based on the data collected from Sina Weibo, one of the most popular microblogging services in China. We find that the daily total amount of user resources is finite and users’ attention transfers from one topic to another. This shows evidence on the competitions between multiple dynamical topics. According to these empirical observations, we develop a competition-based multi-topic information propagation model without social network structure. This model is built based on general mechanisms of resource competitions, i.e. attracting and distracting users’ attention, and considers the interactions of multiple topics. Simulation results show that the model can effectively produce topics with temporal popularity similar to the real data. The impact of model parameters is also analysed. It is found that topic arrival rate reflects the strength of competitions, and topic fitness is significant in modelling the small scale topic propagation.

A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks

2019 ◽  
Vol 30 (1) ◽  
pp. 117-132
Author(s):  
Prasanta Bhattacharya ◽  
Tuan Q. Phan ◽  
Xue Bai ◽  
Edoardo M. Airoldi
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
User Behavior ◽  
Content Generation

Online Social Networks

2014 ◽  
pp. 466-485
Author(s):  
Ana Torres ◽  
Francisco Vitorino Martins
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Consumer Behavior ◽  
Network Structure ◽  
Broad Spectrum ◽  
Online Social Networks ◽  
Market Information ◽  
Social Network Structure ◽  
Potential Market ◽  
Analysis And Synthesis

The chapter is conceptual, based on analysis and synthesis of social network theory and e-consumer literature. Despite a broad spectrum of disciplines that investigate social networks and the interest of marketing practitioners in the consequences of social networks, there are still areas open for research into networked-consumer behavior in marketing. Based on previous theoretical and empirical research, this study examines and discusses the influence of social network structure and ties in matched dyads, recommendation diffusion, social contagion and co-consumption influence, and individual motivations to spread market information. The chapter proposes a theory of matched dyadic ties in close networks of connections as a proxy for information about the potential market that is difficult and expensive for businesses to measure or access directly.

scholarly journals User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction

2019 ◽  
Author(s):  
Fabiola S. F. Pereira ◽  
Gina M. B. Oliveira ◽  
João Gama
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Temporal Dynamics ◽  
User Preferences ◽  
User Preference ◽  
Modeling And Prediction ◽  
Social Network Structure ◽  
Rich Information ◽  
Over Time

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.

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