Do Directionality and Network Size Affect Network Structure in Online Social Networks?

Misinformation, Radicalization and Hate Through the Lens of Users

10.5753/ctd.2020.11373 ◽

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.

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

IEEE Access ◽

10.1109/access.2017.2717921 ◽

2017 ◽

Vol 5 ◽

pp. 12031-12040 ◽

Cited By ~ 12

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

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Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

The Neurohospitalist ◽

10.1177/1941874420945889 ◽

2020 ◽

Vol 11 (1) ◽

pp. 18-24

Author(s):

Morgan Prust ◽

Abby Halm ◽

Simona Nedelcu ◽

Amber Nieves ◽

Amar Dhand

Keyword(s):

Social Support ◽

Social Networks ◽

Social Network ◽

Social Isolation ◽

Network Structure ◽

Network Size ◽

Personal Network ◽

Mapping Tool ◽

Social Network Structure ◽

Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

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

Journal of Information Science ◽

10.1177/0165551516642928 ◽

2016 ◽

Vol 43 (3) ◽

pp. 342-355 ◽

Cited By ~ 3

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.

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ESTIMATING USER INFLUENCE IN ONLINE SOCIAL NETWORKS SUBJECT TO INFORMATION OVERLOAD

International Journal of Modern Physics B ◽

10.1142/s0217979214500040 ◽

2014 ◽

Vol 28 (03) ◽

pp. 1450004 ◽

Cited By ~ 9

Author(s):

PEI LI ◽

YUNCHUAN SUN ◽

YINGWEN CHEN ◽

ZHI TIAN

Keyword(s):

Social Networks ◽

Information Processing ◽

Theoretical Analysis ◽

Online Social Networks ◽

Information Overload ◽

Network Size ◽

Finite Buffer ◽

User Influence ◽

Diffusion Dynamics ◽

The Impact

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

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The Impacts of Network Structure on User Activity Level in Online Social Networks

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) ◽

10.1109/wi-iat.2015.201 ◽

2015 ◽

Cited By ~ 1

Author(s):

Yi Long ◽

Victor O. K. Li ◽

Guolin Niu ◽

Zhiyi Lu

Keyword(s):

Social Networks ◽

Network Structure ◽

Online Social Networks ◽

Activity Level ◽

User Activity

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Social Network Structure Is Related to Functional Improvement From Home-Based Telerehabilitation After Stroke

Frontiers in Neurology ◽

10.3389/fneur.2021.603767 ◽

2021 ◽

Vol 12 ◽

Author(s):

Archana Podury ◽

Sophia M. Raefsky ◽

Lucy Dodakian ◽

Liam McCafferty ◽

Vu Le ◽

...

Keyword(s):

Social Networks ◽

Social Network ◽

Depressive Symptoms ◽

Network Structure ◽

Network Size ◽

Network Density ◽

Stroke Patients ◽

Historical Cohort ◽

Home Based ◽

Social Network Structure

Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms.Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TR patients with a historical cohort of 176 stroke patients who did not receive any TR to identify social network differences.Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TR patient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks.Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TR patients had larger and more open social networks than stroke patients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.

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