Trust in open versus closed social media: The relative influence of user- and marketer-generated content in social network services on customer trust

2017 ◽  
Vol 34 (5) ◽  
pp. 550-559 ◽  
Author(s):  
Boreum Choi ◽  
Inseong Lee
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relative Influence ◽  
Network Services ◽  
Social Network Services ◽  
Open Versus ◽  
Customer Trust

Social Media and Creativity

2017 ◽  
pp. 181-202
Author(s):  
Antonín Pavlíček
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Linear Regression ◽  
Success Factors ◽  
Descriptive Analysis ◽  
Common Factors ◽  
Network Services ◽  
Video Sharing ◽  
Social Network Services

The usage of social media is vital part of businesses practices and lives of individuals today, tourism being not exception. Yet, despite the wide reach of social networks there is a lack of understanding which factors contribute to becoming an influencer on social network services. This chapter particularly focuses on the largest video-sharing platform YouTube. It analyzes common success factors in three different countries: Canada, Germany, Italy and concludes by explaining which factors can be considered as relevant in order to succeed on YouTube. The objective is to find common factors which enable Youtubers to succeed. Predict which quantitative and qualitative elements can actually influence the success of a Youtuber and through ANOVA, Descriptive Analysis and Linear Regression find if there's actually a link between these elements and the number of subscribers. Lastly, it will try to assess on three case studies, how different tourist destination use the power of YouTube.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

Face hallucination and recognition in social network services

2014 ◽  
Vol 71 (6) ◽  
pp. 2035-2049 ◽  
Author(s):  
Feng Jiang ◽  
Seungmin Rho ◽  
Bo-Wei Chen ◽  
Xiaodan Du ◽  
Debin Zhao
Keyword(s):  
Social Network ◽  
Network Services ◽  
Face Hallucination ◽  
Social Network Services
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