Targeted Advertising Based on Social Network Analysis

2014 ◽  
Vol 488-489 ◽  
pp. 1306-1309 ◽  
Author(s):  
Jing Fang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Personal Information ◽  
Analysis Data ◽  
Big Data Analysis ◽  
Targeted Advertising ◽  
Separation Theory ◽  
Internet Advertisement ◽  
Six Degrees Of Separation

Adverting is one of the most important profit models in internet world. With more than ten years development, internet advertisement becomes smarter than ever and RTB (Real Time Bidding) is becoming the major share in the whole internet advertisement. Under RTB environment, advertisers need more accurate and efficient advertising technology than before. Targeted advertising integrates game theory, big data analysis, data mining and advertising technology and helps to publish advertisement to audiences precisely. Social network is the reflection of real world on internet built on six degrees of separation theory, and it has massive users and enormous access every day and thus collects massive personal information which can help to improve targeted advertising. This paper presents a framework to use social network analysis to improve targeted advertising and introduces clustering and cosine similarity as specified algorithm in the framework.

A Study of Personal Information Handler Based on Social Network Analysis

2016 ◽  
Vol 13 (2) ◽  
pp. 143-154
Author(s):  
Young-Hee Kim ◽  
Jun-Suk Hong ◽  
Hwan-ju Cha ◽  
Kwang-Ho Kook
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Personal Information

scholarly journals Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

2022 ◽  
Vol 14 (1) ◽  
pp. 477
Author(s):  
Sung-Un Park ◽  
Jung-Woo Jeon ◽  
Hyunkyun Ahn ◽  
Yoon-Kwon Yang ◽  
Wi-Young So
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Student Athletes ◽  
Big Data Analysis ◽  
Degree Centrality

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

Network Ethnography as an Approach for the Study of New Governance Structures in Education

2019 ◽  
Author(s):  
Catarina Player-Koro
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Digital Media ◽  
Social Relations ◽  
Analysis Data ◽  
Fundamental Principle ◽  
Governance Structures ◽  
New Governance ◽  
Being Moved

Network ethnography was first developed for the study of organizations built around digital media, and is an amalgam of different research methods derived from traditional ethnography and social network analysis. It was then further adapted to study contemporary policy mobility and governance structures, and could be summarized as an adaptation of ethnographic methods to the way contemporary organizations and associations are working due to the globalization and digitalization of society. Network ethnography involves a mapping of the policy field under study using techniques from social network analysis. Data production and analysis of mobilities and interactions within the network are conducted with network ethnography, a method that shares the fundamental principle of ethnography as a tradition. This allows the researcher to analyze network activities and evolutions, how social relations are established and performed, and how policy is being moved—and fixed—through these activities.

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