• Journal of Information Technology Applications and Management
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• v.20 no.1
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• pp.53-66
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• 2013

Social reason was mostly the motivation for using Facebook among Generation Y, but results on Generation X were still inconclusive due to limited studies. By using content analysis of 93 participants' written statements gathered on April to May 2012, this study compared motivations among Facebookers from two generations. The study finds that Generation X and Y shared similar motivations. The majority of both generations use Facebook to keep in touch with their friends, as well as to seek updated information. A smaller number use Facebook for entertainment, online business, and self-expression. In addition, participants were willing to continue using Facebook in the future despite the fact that they had Twitter accounts.

• International Journal of Advanced Culture Technology
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• v.3 no.2
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• pp.50-57
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• 2015

An online survey was conducted to assess motivations for using, reliance on, and perceived credibility of political blogs and microblogs during both the Taiwanese general election of 2009 (the blog epoch) and the presidential elections of 2012 (the microblog epoch). Results indicated higher reliance on and motivation for using political blogs than microblogs. Blogs were also perceived to be more credible than microblogs. Respondents who primarily engaged in blogging for information purposes were more likely to judge candidate blogs as highly credible, whereas interest in entertainment emerged as the strongest predictor of the perceived credibility of microblogs. This research also provided quantitative evidence showing how users viewed blogs and microblogs differently in the context of political campaigns. The aim is to explore the pros and cons of blogging and microblogging as a tool for political communication.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.1
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• pp.24-28
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• 2010

The Quality of Experience (QoE) of next Generation wired/wireless network services based upon IP networking is becoming a popular issue in recent years. The user experience of Internet services such as IPTV, online game, web surfing and etc, are becoming the most desirable factors to service providers to improve service performance and customer's satisfaction. However, collecting user experience from customers and obtaining the QoE parameters from the Quality of Service (QoS) parameters such as bandwidth, delay, jitter or admission control algorithm, are difficult subjects because of the various service types and user characteristics. In this paper, we propose a framework which contains service classification, QoE analysis and service enhancement steps for a suitable QoE measurement and management protocol. We define the user satisfaction indicators of the Internet services, classify the categories of each type of services, and analyse the Key Performance Indicator (KPI) in each type of services to perform the QoS parameters and improving the service qualities.

• Journal of Information Science Theory and Practice
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• v.4 no.2
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• pp.6-20
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• 2016

In the last few years, a new type of synchronous social networking services (SNSs) has emerged—social live streaming services (SLSSs). Studying SLSSs is a new and exciting research field in information science. What information behaviors do users of live streaming platforms exhibit? In our empirical study we analyzed information production behavior (i.e., broadcasting) as well as information reception behavior (watching streams and commenting on them). We conducted two quantitative investigations, namely an online survey with YouNow users (N = 123) and observations of live streams on YouNow (N = 434). YouNow is a service with video streams mostly made by adolescents for adolescents. YouNow users like to watch streams, to chat while watching, and to reward performers by using emoticons. While broadcasting, there is no anonymity (as in nearly all other WWW services). Synchronous SNSs remind us of the film The Truman Show, as anyone has the chance to consciously broadcast his or her own life real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1626-1638
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• 2019

Social Networking Sites (SNS) provide people with unique online social interaction environments where users can disclose their thoughts, feelings, and opinions to their personal contacts. Although previous studies have suggested that such activities produce positive effects on SNS user well-being, this study considered potential negative effects by investigating the relationship between SNS use and depression. In particular, This stydy examined how specific activities are related to different types of social comparison (upward/downward/horizontal) and how these different types of social comparison influence depressed moods among college students. The analysis of a survey of 245 Instagram users found that (1) looking at other people's status updates and commenting on other people's photos influences upward social comparison, (2) frequency of Instagram use predicts upward/downward/horizontal social comparison, and (3) upward social comparison was postively associated with depression, while downward social comparison was negatively associated with depression. Furthermore, the path anlaysis show that social comparison mediates the effect of Instagram use on depression. It suggests that Instagram use does not directly increase depression but it can lead to depression when social comparison on Instagram triggers depression.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• Fashion & Textile Research Journal
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• v.14 no.4
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• pp.614-628
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• 2012

The purpose of this study was to analyze research trends and make suggestions regarding the future of information technology (IT) in the fashion industry. In this study, 437 papers written regarding IT fashion from five major journals published between 2000 and 2011 were examined. The research areas were then organized by subject and keyword, and divided into 16 high-context categories. Two IT fashion maps were constructed, one from a fashion consumer's perspective, and the other based on the fashion industry's supply chain. This study identified important trends in IT fashion such as: 3D scanners, 3D digital renderings of the human form, 3D digital garments, smart garments, mass customization, production automation, online shopping, home shopping, online communities, e-commerce, digital media, virtual reality, e-tail, the digital generation, E-CRM, and education. Data from body scans was collected and applied to production, and research on smart textiles was also carried out. As for IT fashion's service areas, the majority of the research focused on online shopping or online communication. Additionally, research done on avatars and cyber space, and studies on social networking services are shown. The results of this study indicated that a new field of research has opened and that current research has been developing. Also, this study showed what is needed to expand and strengthen IT fashion.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.375-380
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• 2016

The proliferation of online social networking services (OSNSs) and smartphones has enabled people to easily make friends with a large number of users in the online communities, and interact with each other. This leads to an increase in the usage rate of OSNSs. However, individuals who have immersed into their digital lives, prioritizing the virtual world against the real one, become more and more isolated in the physical world. Thus, their socialization processes that are undertaken only through lots of face-to-face interactions and trial-and-errors are apt to be neglected via 'Add Friend' kind of functions in OSNSs. In this paper, we present a friend recommendation system based on the on/off-line contextual information for the OSNS users to have more serendipitous offline interactions. In order to accomplish this, we modeled both offline information (i.e., place visit history) collected from a user's smartphone on a 3D tensor, and online social data (i.e., friend relationships) from Facebook on a matrix. We then recommended like-minded people and encouraged their offline interactions. We evaluated the users' satisfaction based on a real-world dataset collected from 43 users (12 on-campus users and 31 users randomly selected from Facebook friends of on-campus users).

• Journal of Digital Convergence
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• v.11 no.12
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• pp.479-485
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• 2013

Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance isolated platforms called virtual machines. Through server virtualization software, applications servers are encapsulated into VMs, and deployed with APIs on top generalized pools of CPU and memory resources. Networking and security have been moved to a software abstraction layer that transformed computing, network virtualization. And it paves the way for enterprise to rapidly deploy networking and security for any application by creating the virtual network. Stochastic reward net (SRN) is an extension of stochastic Petri nets which provides compact modeling facilities for system analysis. In this paper, we develop SRN model of network virtualization based on virtual switch. Measures of interest such as switching delay and throughput are considered. These measures are expressed in terms of the expected values of reward rate functions for SRNs. Numerical results are obtained according to the virtual switch capacity and number of active VMs.