Integrating Online Social Network Analysis in Personalized Web Search

2012 ◽  
pp. 589-613
Author(s):  
Omair Shafiq ◽  
Tamer N. Jarada ◽  
Panagiotis Karampelas ◽  
Reda Alhajj ◽  
Jon G. Rokne
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Web Search ◽  
Online Social Network

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.

On personalizing Web search using social network analysis

2015 ◽  
Vol 314 ◽  
pp. 55-76 ◽  
Author(s):  
Omair Shafiq ◽  
Reda Alhajj ◽  
John G. Rokne
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Web Search

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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