scholarly journals Network Structure Impacts the Synchronization of Collective Beliefs

2021 ◽  
Vol 21 (5) ◽  
pp. 431-448
Author(s):  
Madalina Vlasceanu ◽  
Michael J. Morais ◽  
Alin Coman
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Belief Change ◽  
Community Members ◽  
Separation Effect ◽  
Relevant Evidence ◽  
Degree Of Separation ◽  
Collective Beliefs ◽  
Post Test ◽  
Clustered Networks

Abstract People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post-test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.

scholarly journals Network Structure Impacts the Synchronization of Collective Beliefs

2020 ◽  
Author(s):  
Madalina Vlasceanu ◽  
Alin Coman
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Belief Change ◽  
Community Members ◽  
Separation Effect ◽  
Relevant Evidence ◽  
Degree Of Separation ◽  
Collective Beliefs ◽  
Post Test ◽  
Clustered Networks

People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post- test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non- clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.

scholarly journals Network Structure Impacts the Synchronization of Collective Beliefs

2020 ◽  
Author(s):  
Madalina Vlasceanu ◽  
Alin Coman
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Belief Change ◽  
Community Members ◽  
Separation Effect ◽  
Relevant Evidence ◽  
Degree Of Separation ◽  
Collective Beliefs ◽  
Post Test ◽  
Clustered Networks

Abstract People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post-test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.

scholarly journals Political and Non-Political Belief Change Elicits Behavioral Change

2021 ◽  
Author(s):  
Madalina Vlasceanu ◽  
Casey McMahon ◽  
Jay Joseph Van Bavel ◽  
Alin Coman
Keyword(s):  
Behavioral Change ◽  
Belief Change ◽  
Relevant Evidence ◽  
Task Behavior ◽  
Preventative Health ◽  
Post Test ◽  
Health Related ◽  
Causal Impact ◽  
The Relationship

Beliefs have long been posited to be a predictor of behavior. However, empirical evidence of the relationship between beliefs and behaviors has been mostly correlational in nature and provided conflicting findings. Here, we investigated the causal impact of beliefs on behaviors across three experiments (N=659). Participants rated the accuracy of a set of health-related statements (belief pre-test) and chose corresponding campaigns to which they could donate funds in an incentivized choice task (behavior pre-test). They were then provided with relevant evidence in favor of the correct statements and against the incorrect statements. Finally, they rated the accuracy of the initial set of statements again (belief post-test) and were given a chance to change their donation choices (behavior post-test). We found that evidence changed beliefs and this, in turn, led to behavioral change. In two pre-registered follow-up experiments, we replicated these findings with politically charged topics, and found a partisan asymmetry in the effect of belief change on behavioral change in Democrats (but not in Republicans). We discuss the implications of this work for interventions aimed at promoting constructive behaviors such as recycling, donating, or employing preventative health measures.

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals The Network Structure of Police Misconduct

2019 ◽  
Vol 5 ◽  
pp. 237802311987979 ◽  
Author(s):  
George Wood ◽  
Daria Roithmayr ◽  
Andrew V. Papachristos
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Police Officers ◽  
Police Misconduct ◽  
Police Departments ◽  
Top Down ◽  
Network Ties ◽  
Individual Level ◽  
Factors Associated ◽  
Using Data

Conventional explanations of police misconduct generally adopt a microlevel focus on deviant officers or a macrolevel focus on the top-down organization of police departments. Between these levels are social networks of misconduct. This study recreates these networks using data on 16,503 complaints and 15,811 police officers over a six-year period in Chicago. We examine individual-level factors associated with receiving a complaint, the basic properties of these misconduct networks, and factors related to officer co-naming in complaints. We find that the incidence of police misconduct is associated with attributes including race, age, and tenure and that almost half of police officers are connected in misconduct ties in broader networks of misconduct. We also find that certain dyadic factors, especially seniority and race, strongly predict network ties and the incidence of group misconduct. Our results provide actionable information regarding possible ways to leverage the co-complaint network structure to reduce misconduct.

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

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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

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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