Personality and emerging adults' friend selection on social networking sites: A social network analysis perspective

PsyCh Journal ◽  
2020 ◽  
Author(s):  
Yixin Zhou ◽  
Zheng Zhang ◽  
Kexin Wang ◽  
Shuang Chen ◽  
Mingjie Zhou ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Emerging Adults ◽  
Social Networking Sites

scholarly journals Social Network Analysis

2012 ◽  
Vol 4 (3) ◽  
pp. 46-58 ◽  
Author(s):  
Darren Quinn ◽  
Liming Chen ◽  
Maurice Mulvenna
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Social Networking Sites ◽  
Interaction Analysis ◽  
Future Directions ◽  
Core Concepts ◽  
The Common ◽  
Art Research

Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.

Academics and their online networks: Exploring the role of academic social networking sites

First Monday ◽  
2014 ◽  
Author(s):  
Katy Jordan
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Network Analysis ◽  
Social Networking ◽  
Network Structure ◽  
Social Networking Sites ◽  
Rapid Rise ◽  
Online Networks ◽  
Peripheral Position

The rapid rise in popularity of online social networking has been followed by a slew of services aimed at an academic audience. This project sought to explore network structure in these sites, and to explore trends in network structure by surveying participants about their use of sites and motivations for making connections. Social network analysis revealed that discipline was influential in defining community structure, while academic seniority was linked to the position of nodes within the network. The survey revealed a contradiction between academics use of the sites and their position within the networks the sites foster. Junior academics were found to be more active users of the sites, agreeing to a greater extent with the perceived benefits, yet having fewer connections and occupying a more peripheral position in the network.

Centrality Measures and Academic Achievement in Computerized Classroom Social Networks

2017 ◽  
Vol 56 (4) ◽  
pp. 589-618 ◽  
Author(s):  
Iris Reychav ◽  
Daphne Ruth Raban ◽  
Roger McHaney
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Learning Task ◽  
Learning Performance ◽  
Centrality Measures ◽  
Group Consensus ◽  
Learning Assessments ◽  
Individual Assessment

The current empirical study examines relationships between network measures and learning performance from a social network analysis perspective. We collected computerized, networking data to analyze how 401 junior high students connected to classroom peers using text- and video-based material on iPads. Following a period of computerized interaction, learning assessments were taken at individual or group consensus levels. Social network analysis suggested highly connected students became information sources with higher individual assessment achievements. Students receiving information from central sources exhibited higher achievements in group consensus treatments. Students acting as bridges between others on the network regulated themselves better and achieved higher academic outcomes. However, a subset of students were motivated by social interaction rather than learning task. This finding, consistent with general social networking research, cautions educators to ensure socializing does not override learning objectives when using classroom social networking.

scholarly journals Brand influence online: cross-platform social network analysis on the enablement of effective brand communities

2021 ◽  
Author(s):  
Cameron Munro
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Methodological Approach ◽  
Brand Community ◽  
Brand Communities ◽  
Online Brand Community ◽  
Network Properties ◽  
Cross Platform

This paper aims to provide a systematic methodological approach for online brand community assessment across multiple social networking platforms. Analysis of influential brands was conducted utilizing a social network analysis (SNA) perspective. Brand communities were scored based on network properties and content analysis. Background research provided a framework of recommended community enablement strategies to determine what type of content and approach is most conducive to brand community proliferation. Based on network analysis and on congruency of following academically suggested community enablement triggers and behavioural dimensions, it was determined that the most effective brand at enabling community across all platforms within the study was Yeti Coolers. Instagram was the focal platform providing engaging content to be shared across networks

Social Networks in Information Systems

2010 ◽  
pp. 392-410 ◽  
Author(s):  
Hernâni Borges de Freitas ◽  
Alexandre Barão ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Information Exchange ◽  
Group Processes ◽  
Communication Tools ◽  
Social Entities

A social network represents a set of social entities that interact through relationships like friendship, co-working, or information exchange. Social Network Analysis studies the patterns of relationships among social entities and can be used to understand and improve group processes. The arrival of new communication tools and networking platforms, especially the Web 2.0 Social Networking Services, opened new opportunities to explore the power of social networks inside and outside organizations. This chapter surveys the basic concepts of social networks methods, approaches, tools, and services. In particular, this chapter analyzes state-of-the-art social networks, explaining how useful Social Network Analysis can be in different contexts and how social networks can be represented, extracted, and analyzed in information systems.

Sexual partners’ referral for HIV testing through social networking platforms: A social network analysis (Preprint)

2021 ◽  
Author(s):  
Piao-Yi Chiou ◽  
Chien-Ching Hung ◽  
Chien-Yu Chien
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Hiv Testing ◽  
Sexual Partners ◽  
Hiv Positive ◽  
Network Graph ◽  
Hiv Test ◽  
The Social

BACKGROUND Men who have sex with men (MSM) who undergo HIV voluntary counselling and testing (VCT) usually self-identify as having many sexual partners and as being exposed to risky sexual networks. Limited research discusses the application of motivative interviews and convenience referral platforms for MSM to facilitate the referral of sexual partners to HIV testing. The social network analysis (SNA) of such referral strategy remains unclear. OBJECTIVE To evaluate the effects of sexual partners’ referral through the social networking platforms for HIV testing and the test results after having elicited interviews with MSM, compare the different characteristics and risk behaviors of the subgroups, and to explore the unknown sexual affiliations through visualizing and quantifying the social network graph. METHODS This is a cohort study design. Purposeful sampling was used to recruit the index subjects at a community HIV screening station that is frequented by MSM in Taipei City on Friday and Saturday nights. Respondent-driven sampling was used to recruit the sexual partners. Partner-elicited interviews were conducted by trained staff before the VCT to motivate MSM to become the referrer to refer sexual partners via the Line application (app) or to disclose the account and profile on the relevant social networking platforms. The rapid HIV test was delivered to the referred sexual partners and the recruitment process continued in succession until leads were exhausted. RESULTS After the interviews, 28.2% (75/266) MSM were successfully persuaded to be index subjects in the first wave, referring 127 sexual partners via the Line app for the rapid HIV testing, and disclosing 40 sexual partners. The index subjects and the tested sexual partners exhibited higher numbers of sexual partners (F = 3.83, P = .023), higher frequencies of anal intercourse (F = 10.10, P < .001), and higher percentages of those who had not previously received HIV testing (x2 = 6.106, P = .047) when compared to the subjects without referrals. The newly HIV-seropositivity rate of tested sexual partners was 2.4%, which was higher than the other two groups. The SNA discovered four types of sexual affiliation, namely chain, Y, star, and complicated type. The complicated type had the most HIV-positive nodes. There were 26.87% (43/160) of the HIV-negative sexual partners who had sexual affiliations with HIV-positive nodes; 40% of them (10/25) were untested sexual partners, who had directly sexual affiliation with HIV-positive node. Four transmission bridge was found in the network graph. CONCLUSIONS Partner-elicited interviews can effectively promote the referral or disclosure sexual partners via social networking platforms for HIV testing and HIV case finding, and can reveal unknown sexual affiliations of MSM that can facilitate the development of a tailored prevention program.

Social Network Analysis

2016 ◽  
pp. 237-265 ◽  
Author(s):  
Paramita Dey ◽  
Sarbani Roy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Group Formation ◽  
Social Structures ◽  
Node Degree ◽  
Network Characteristics ◽  
Meaningful Information ◽  
The Subject

Social Network Analysis (SNA) looks at how our world is connected. The mapping and measuring of connections and interactions between people, groups, organizations and other connected entities are very significant and have been the subject of a fascinating interdisciplinary topic . Social networks like Twitter, Facebook, LinkedIn are very large in size with millions of vertices and billions of edges. To collect meaningful information from these densely connected graph and huge volume of data, it is important to find proper topology of the network as well as analyze different network parameters. The main objective of this work is to study network characteristics commonly used to explain social structures. In this chapter, we discuss all important aspect of social networking and analyze through a real time example. This analysis shows some distinguished parameters like number of clusters, group formation, node degree distribution, identifying influential leader/seed node etc. which can be used further for feature extraction.

Applications of Data Mining in Dynamic Social Network Analysis

2013 ◽  
pp. 110-121
Author(s):  
Manish Kumar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Knowledge Discovery ◽  
Online Social Networking ◽  
Dynamic Social Network ◽  
Business Goals

Social Networks are nodes consisting of people, groups and organizations growing dynamically. The growth is horizontal as well as vertical in terms of size and number. Social network analysis has gained success due to online social networking and sharing sites. The accessibility of online social sites such as MySpace, Facebook, Twitter, Hi5, Friendster, SkyRock and Beb offer sharing and maintaining large amount of different data. Social network analysis is focused on mining such data i.e. generating pattern of people’s interaction. The analysis involves the knowledge discovery that helps the sites as well as users in terms of usage and business goals respectively. Further it is desired that the process must be privacy preserving. This chapter describes the various mining techniques applicable on social networks data.

Social Networks in Information Systems

2011 ◽  
pp. 127-144
Author(s):  
Hernâni Borges de Freitas ◽  
Alexandre Barão ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Information Exchange ◽  
Group Processes ◽  
Communication Tools ◽  
Social Entities

A social network represents a set of social entities that interact through relationships like friendship, co-working, or information exchange. Social Network Analysis studies the patterns of relationships among social entities and can be used to understand and improve group processes. The arrival of new communication tools and networking platforms, especially the Web 2.0 Social Networking Services, opened new opportunities to explore the power of social networks inside and outside organizations. This chapter surveys the basic concepts of social networks methods, approaches, tools, and services. In particular, this chapter analyzes state-of-the-art social networks, explaining how useful Social Network Analysis can be in different contexts and how social networks can be represented, extracted, and analyzed in information systems.

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