Offline netwerken, online pesten

2013 ◽  
Vol 41 (1) ◽  
Author(s):  
Denis Wegge ◽  
Heidi Vandebosch ◽  
Steven Eggermont
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
School Context ◽  
Traditional Bullying ◽  
Online Bullying ◽  
Best Friends ◽  
Being Bullied

Offline networks, online bullying: a social network analysis of cyberbullying in a school context Offline networks, online bullying: a social network analysis of cyberbullying in a school context Young adolescents increasingly bully each other in ‘cyberspace’, which has raised a significant amount of academic attention. The present study contributes to this body of research by linking cyberbullying to young people’s offline social relationships. It considers the influence of social position on victimization, and the interactions between online victims and perpetrators. For this purpose an entire grade of 174 pupils, age 12 and 13, was surveyed. The pupil’s social networks were analyzed to predict who is being victimized and by whom adolescents are cyberbullied. Results indicate that victims of cyberbullying have fewer mutual friends at school, regardless of traditional bullying involvement. In contrast, their number of mutual ‘best friends’ does not significantly differ. Furthermore, cyberbullying proves to be a true extension of offline bullying; victims are being bullied by the same perpetrators offline and online, which is particularly problematic. In sum, offline relationships and interactions do influence online bullying.

scholarly journals Who bullies whom online: A social network analysis of cyberbullying in a school context

2014 ◽  
Vol 39 (4) ◽  
Author(s):  
Denis Wegge ◽  
Heidi Vandebosch ◽  
Steven Eggermont
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Social Context ◽  
Network Analysis ◽  
School Context ◽  
Future Research ◽  
Traditional Bullying ◽  
Online Bullying ◽  
The Social ◽  
Bullying Behavior

AbstractYoung adolescents’ online bullying behavior has raised a significant amount of academic attention. Nevertheless, little is known about the social context in which such negative actions occur. The present paper addresses this issue and examines how the patterns of traditional bullying and cyberbullying are related, and how electronic forms of bullying can be linked to the social context at school. To address these questions, social network analysis was applied to examine the networks of social interactions and (cyber)bullying among an entire grade of 1,458 thirteen- to fourteen-year-old pupils. The results show that (1) cyberbullying is an extension of traditional bullying as victims often face the same perpetrators offline and online, (2) there is evidence of mutual cyberbullying among youngsters, and (3) cyberbullying is more likely to occur in same-gender and same-class students. The implications for future research and prevention of cyberbullying are discussed.

Web Mining and Social Network Analysis

Data Mining ◽  
2013 ◽  
pp. 326-335
Author(s):  
Roberto Marmo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Web Mining ◽  
Social Network Mining ◽  
Data Mining Algorithms ◽  
Instant Messenger ◽  
Mining Algorithms

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.

Web Mining and Social Network Analysis

2011 ◽  
pp. 202-211 ◽  
Author(s):  
Roberto Marmo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Web Mining ◽  
Social Network Mining ◽  
Data Mining Algorithms ◽  
Instant Messenger ◽  
Mining Algorithms

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.

Humans, Plants, and Networks

2016 ◽  
Vol 7 (1) ◽  
pp. 107-128 ◽  
Author(s):  
Laura Calvet-Mir ◽  
Matthieu Salpeteur
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Collection ◽  
Social Relationships ◽  
Social Relations ◽  
Local Ecological Knowledge ◽  
Ecological Knowledge ◽  
Longitudinal Data Collection

ABSTRACTIn recent years, Social Network Analysis (SNA) has increasingly been applied to the study of complex human-plant relations. This quantitative approach has enabled a better understanding of (1) how social networks help explain agrobiodiversity management, and (2) how social relations influence the transmission of local ecological knowledge (LEK) related to plants. In this paper, we critically review the most recent works pertaining to these two lines of research. First, our results show that this fast-developing literature proposes new insights on local agrobiodiversity management mechanisms, as well as on the ways seed exchange systems are articulated around other social relationships, such as kinship. Second, current works show that inter-individual connections affect LEK transmission, the position of individuals in networks being related to the LEK they hold. We conclude by stressing the importance of combining this method with comprehensive approaches and longitudinal data collection to develop deeper insights into human-plant relations.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

Predicting Violent Victimization Using Social Network Analysis From Police Data

2021 ◽  
Vol 36 (3) ◽  
pp. 436-454
Author(s):  
Andrew M. Fox ◽  
Kenneth J. Novak ◽  
Tinneke Van Camp ◽  
Chadley James
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Violent Crime ◽  
Violent Victimization ◽  
Crime Victimization ◽  
Effective Prevention ◽  
Police Data ◽  
Assault Victims

Extant research suggests that membership in crime networks explains vulnerability to violent crime victimization. Consequently, identifying deviant social networks and understanding their structure and individual members' role in them could provide insight into victimization risk. Identifying social networks may help tailor crime prevention strategies to mitigate victimization risks and dismantle deviant networks. Social network analysis (SNA) offers a particular means of comprehending and measuring such group-level structures and the roles that individuals play within them. When applied to research on crime and victimization, it could provide a foundation for developing precise, effective prevention, intervention, and suppression strategies. This study uses police data to examine whether individuals most central to a deviant social network are those who are most likely to become victims of violent crime, and which crime network roles are most likely to be associated with vulnerability to violent victimization. SNA of these data indicates that network individuals who are in a position to manage the flow of information in the network (betweenness centrality), regardless of their number of connections (degree centrality), are significantly more likely to be homicide and aggravated assault victims. Implications for police practice are discussed.

Entanglement, Materiality and the Social Organisation of Construction Workers in Classical Athens

2018 ◽  
pp. 512-528
Author(s):  
Diane Harris Cline
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Theory ◽  
Actor Network Theory ◽  
Fifth Century ◽  
Actor Network ◽  
The Social ◽  
Building Program

This chapter views the “Periclean Building Program” through the lens of Actor Network Theory, in order to explore the ways in which the construction of these buildings transformed Athenian society and politics in the fifth century BC. It begins by applying some Actor Network Theory concepts to the process that was involved in getting approval for the building program as described by Thucydides and Plutarch in his Life of Pericles. Actor Network Theory blends entanglement (human-material thing interdependence) with network thinking, so it allows us to reframe our views to include social networks when we think about the political debate and social tensions in Athens that arose from Pericles’s proposal to construct the Parthenon and Propylaea on the Athenian Acropolis, the Telesterion at Eleusis, the Odeon at the base of the South slope of the Acropolis, and the long wall to Peiraeus. Social Network Analysis can model the social networks, and the clusters within them, that existed in mid-fifth century Athens. By using Social Network Analysis we can then show how the construction work itself transformed a fractious city into a harmonious one through sustained, collective efforts that engaged large numbers of lower class citizens, all responding to each other’s needs in a chaine operatoire..

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