scholarly journals A Qualitative Analysis of a Team Fortress 2 Social Network

2018 ◽  
Author(s):  
Ilan Tollman ◽  
Motty Yaffe
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Qualitative Analysis ◽  
Online Social Network ◽  
Computer Game ◽  
Relevant Information ◽  
Social Consequence ◽  
Academic Field ◽  
Gaming Culture

The Gaming Industry is one of the most dynamic and fasted growing of our time, from the arcade culture to the widespread integration of household gaming consoles and home gaming PCs, whose steadily rising popularity, performance and inter-connectivity is allowing for more and more growth in the range and permeability of gaming and gaming culture. With its widespread popularity and its ever-increasing influence, through the acceptance of gaming culture as mainstream, the gaming world is becoming a crucial field of study for better understanding of online communities and social interaction. In this paper we will expand on the academic field of Online Social Network Analysis through the examination of a selected social network from the gaming world as a microcosm of online social interactions. The paper will draw on Social Network Analysis, Sociology and Communications theories to further expand the understanding of on-line interactions on a network scale. Current gaming focused research leads in two main directions, social consequence whether they be pro social or anti social. There is also however a smaller developing trend of study within the Social Network Analysis field that uses the data collected by computer game makers to study network structure as a variable of its own. In this research the findings of this trend within the computer game research will be used to conduct a qualitative Analysis of a small friends list network from Team Fortress 2. The data collected will compared and contrasted with established Sociology and Communications theories, the expected results and variances will then be explained using the findings of Social Network Analysis research.The Research draws the Two Step Flow communications theory (Lazarsfeld, 1948), and a slightly more refined version of the theory (Katz, 1957), to deduce relevant information on the interaction between companies, hardcore gamers and more casual gamers. The study will also draw predominantly on Mark Granovette work on Social Network Analysis.

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.

Understanding Researchers Collaboration in eParticipation using Social Network Analysis

2015 ◽  
Vol 11 (4) ◽  
pp. 38-68
Author(s):  
Eleni Kaliva ◽  
Dimitrios Katsioulas ◽  
Efthimios Tambouris ◽  
Konstantinos Tarabanis
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Area ◽  
Core Network ◽  
Academic Field ◽  
The Social ◽  
High Degree ◽  
Political Priority ◽  
Scientific Collaborations

Over the past years electronic participation (eParticipation) became a political priority worldwide. Consequently, research on the field has dramatically grown. However, eParticipation is still an unconsolidated research area that lacks generally agreed upon definitions, research disciplines, methods and boundaries. The aim of this paper is to contribute to the establishment of the eParticipation identity by investigating the scientific collaborations in the domain. The study of the nature of academic collaboration reveals the structure and the intellectual roots of the research community and the most influential authors. The approach followed in this paper includes the construction of the co-authorship network and the calculation of the social network analysis (SNA) metrics that describe the nature of the collaboration. The results revealed that eParticipation is a rather active academic field in the last decade including a high degree of collaboration and a core network of very influential researchers.

Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks

2012 ◽  
Vol 5 (1) ◽  
pp. 16-34 ◽  
Author(s):  
Marion E. Hambrick
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Home Pages ◽  
Sporting Event ◽  
Sport Industry ◽  
Twitter Users ◽  
Promotional Messages ◽  
Share Information

Sport industry groups including athletes, teams, and leagues use Twitter to share information about and promote their products. The purpose of this study was to explore how sporting event organizers and influential Twitter users spread information through the online social network. The study examined two bicycle race organizers using Twitter to promote their events. Using social network analysis, the study categorized Twitter messages posted by the race organizers, identified their Twitter followers and shared relationships within Twitter, and mapped the spread of information through these relationships. The results revealed that the race organizers used their Twitter home pages and informational and promotional messages to attract followers. Popular Twitter users followed the race organizers early, typically within the first 4 days of each homepage’s creation, and they helped spread information to their respective followers. Sporting event organizers can leverage Twitter and influential users to share information about and promote their events.

scholarly journals Social network analysis and mining: challenges and applications

2021 ◽  
Author(s):  
Bruna P. Fonseca ◽  
Priscila C. Albuquerque ◽  
Fabio Zicker ◽  
Carlos M. Morel
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Practical Knowledge ◽  
Relevant Information ◽  
Critical Perspective ◽  
Research Fields ◽  
New Challenges ◽  
Advanced Computing ◽  
Computing Infrastructure

Social network analysis and mining (SNAM) is a powerful tool to dis- close relevant information hidden in large volumes of raw data. Its application to several research fields, powered by automation and advanced computing infrastructure, expanded its use and brought along new challenges. In this paper, we provide a critical perspective on SNAM’s major challenges, by discussing a few examples. We also address some promising applications that can potentially translate SNAM results into practical knowledge.

Online Social Network Analysis

2019 ◽  
pp. 50-63
Author(s):  
Praveen Kumar Bhanodia ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Structural Features ◽  
Network Properties ◽  
Prediction Systems ◽  
And Behavior

Expansion of online social networks is rapid and furious. Millions of users are appending to it and enriching the nature and behavior, and the information generated has various dimensional properties providing new opportunities and perspective for computation of network properties. The structure of social networks is comprised of nodes and edges whereas users are entities represented by node and relationships designated by edges. Processing of online social networks structural features yields fair knowledge which can be used in many of recommendation and prediction systems. This is referred to as social network analysis, and the features exploited usually are local and global both plays significant role in processing and computation. Local features include properties of nodes like degree of the node (in-degree, out-degree) while global feature process the path between nodes in the entire network. The chapter is an effort in the direction of online social network analysis that explores the basic methods that can be process and analyze the network with a suitable approach to yield knowledge.

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