Analysis of Data on the Internet III – Online Social Network Analysis

2014 ◽  
pp. e27-e42
Author(s):  
David Nettleton
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
The Internet

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.

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 of e-complaints in fast food sectors in Turkey

2015 ◽  
Vol 12 (2) ◽  
pp. 235
Author(s):  
Deniz Yüncü ◽  
Hilmi Rafet Yüncü
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Fast Food ◽  
Feedback Mechanism ◽  
Internet Technology ◽  
The Internet ◽  
Consumer Complaints ◽  
Internet Environment

<p>Complaint is defined as an act of reflecting a dissatisfying situation to other side. Consumers act differently while they are expressing their dissatisfaction. Consumer complaints constitute an important feedback mechanism for companies. Thanks to these feedbacks, the firms get a chance to correct the mistake in the process of production and to produce a more satisfying product. Consumers express their complaints about the dissatisfying processes through feedback to the firm, expressing them to their friends, or resentment. With the developments in the Internet technology, it is seen that complaints have become widespread. Consumers let more people know about their complaints by expressing them in the internet environment. Therefore, the firms should attach importance to e-complaints and tolerate the dissatisfaction of their consumers. Although there are some studies about consumer complaints in the tourism area, there are not any studies about fast-food agencies. For this reason, it is aimed to categorize the fast-food agencies in www.sikayetvar.com.</p>

Social Media Metrics in an Academic Setup

2017 ◽  
pp. 116-130
Author(s):  
Tasleem Arif ◽  
Rashid Ali
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Present Status ◽  
The Internet ◽  
Collaboration Networks ◽  
Different Types ◽  
Network Metrics ◽  
Network Analysis Metrics

Social media is perhaps responsible for largest share of traffic on the Internet. It is one of the largest online activities with people from all over the globe making its use for some sort of activity. The behaviour of these networks, important actors and groups and the way individual actors influence an idea or activity on these networks, etc. can be measured using social network analysis metrics. These metrics can be as simple as number of likes on Facebook or number of views on YouTube or as complex as clustering co-efficient which determines future collaborations on the basis of present status of the network. This chapter explores and discusses various social network metrics which can be used to analyse and explain important questions related to different types of networks. It also tries to explain the basic mathematics behind the working of these metrics. The use of these metrics for analysis of collaboration networks in an academic setup has been explored and results presented. A new metric called “Average Degree of Collaboration” has been defined to quantify collaborations within institutions.

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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