scholarly journals An Empirical Study on the Influence of Dormitory on College Students' Social Network: Based on Social Network Analysis

2021 ◽  
Vol 6 (2) ◽  
pp. 66
Author(s):  
Wu Ling ◽  
Liao Kangli
Keyword(s):  
College Students ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Study

scholarly journals The Research of Social Network Analysis on College Students' Interactive Relations

2021 ◽  
Vol 15 (2) ◽  
pp. 49-59
Author(s):  
Chang Chen ◽  
Min Chen
Keyword(s):  
College Students ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Student Affairs ◽  
Interpersonal Relations ◽  
Interaction Network ◽  
Information Support ◽  
Limited Information ◽  
New Information

Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.

An Empirical Study on Filter Bubbles in the YouTube Comments Network

2021 ◽  
Vol 9 (3) ◽  
pp. 52-65
Author(s):  
Dukjin Kim ◽  
Wooyoung Lee ◽  
Dohyung Kim ◽  
Gwangyong Gim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Study ◽  
Information Acquisition ◽  
Recommendation Algorithm ◽  
Video Clips ◽  
Filter Bubble ◽  
Filter Bubbles ◽  
Video Recommendation

Some point out that the influence of YouTube's video recommendation algorithm is causing users to be exposed to only video clips in limited subjects or fields, especially to biased content with opinions that are tilted to one side. However, there is a lack of empirical research on filter bubbles as algorithms in YouTube have not been disclosed. This study indirectly demonstrated the phenomenon of filter bubble on YouTube by extracting comment-based content network between uploaders who posted videos and writers who wrote comments on the video by each subject of the contents. Also, this study analyzed communication patterns between users through social network analysis (SNA). According to the analysis, users' narrow information acquisition and communication phenomenon caused by the filter bubble in YouTube was found.

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