Multidisciplinary Social Networks Research

The rise of Social Networking Services (SNSs) has not only transformed people as well as consumer behavior on the Internet, but also transformed the means by which various enterprises globally conduct their promotional and marketing campaigns. There are a variety of means by which enterprises have launched their marketing campaigns on Social Networking Services, and one of the most common techniques adopted is through extensive advertising campaigns on SNSs. This study seeks to examine consumer behaviors towards advertisements on Social Networking Services. Key factors affecting consumer behaviors include usage pattern, the credibility of a particular Social Networking Service as well as electronic word-of-mouth. This clearly illustrates that in today’s virtual electronic world, social media have progressed from being merely a place to meet people, to being a virtual sales floor. It is unexpected that consumer behaviors are influenced by the electronic word-of-mouth of friends rather than that of strangers.

Knowledge creation has been developing its own characteristics with respect to its antecedents. A research model is proposed and empirically examined that describes knowledge creation by considering creation task-technology fit (CTTF), creation task mode (goal-driven, goal-free, and goal-frame) and information and communication technology (ICT). Based on the data analysis from 258 valid subjects from research institutes, manufacturing industry, and service industry, research findings suggest that (1) subjects from research institutes, manufacturing industry, and service industry are likely to be significantly concerned with the effect of CTTF on creation merits, (2) the relationships between independent variables and dependent ones are almost not moderated by cognitive style, except ICT showing that subjects with the analytical style regard ICT not a significant predicator of creation task-technology fit while those with intuitive style regard so, and (3) result of moderating effect on the creation task mode shows that goal-driven mode does not reveal significant for the analysis-styled subjects. Implications and discussions are also addressed.

For the last decade, knowledge community has become popular resources of knowledge and their measurement also become more complex and critical for users and community managers. In this research, an integrated framework based on BSC (Balanced Score Card) - OKC

BSC

is proposed to evaluate the performance of online knowledge community in four dimensions: customer, internal process, learning and growth, and performance. After conducting Delphi approach and AHP methods, the measurements and their weights are derived. Two typical cases (Wikipedia and Yahoo!Kimo Knowledge

 + 

) that conduct empirical surveys with 822 samples each are used to demonstrate the use of OKC

BSC

. Results and discussion are also addressed.

Customers may feel negative emotion when they experience service failure. The negative emotion may induce unsatisfactory customers to spread negative word-of-mouths (WOM). However, not all unsatisfactory will spread negative WOM. The current study conducted an experimental design to explore the influence of opinion leadership tendency to negative word-of-mouth communication intention. The results revealed that customers will spread negative WOMs when the service failure is serious. However, when the service failure is minor, customers with a higher opinion leadership tendency are with higher intention to spread negative WOMs. The findings of the current are useful in exploring the role of opinion leadership tendency in negative WOM communication.

Online shopping will become increasingly popular as more and more people have been changing their shopping ways from traditional to online shopping. However, sometimes it is difficult for a buyer to decide which seller is good for his/her purchases. Therefore, it is necessary to design a system that can recommend sellers for a buyer to purchase his/her products from them. In this paper, a seller recommendation system using user feedback is proposed. The seller recommendation system is to rank the sellers selling the products specified by buyers. Buyers can specify products and budgets as they need. Finally, the experimental results show that an seller being able to provide excellent services should be experienced for more than 10 years and our system can actually reflect the real situation by contrasting the top rated sellers recognized by the eBay site.

The study is an exploratory study to investigate interactions between the strategic alliances of community colleges and community development associations on development of local culture industry. Their strategic alliances are identified through the methods such as fuzzy-set qualitative comparative analysis (fs/qca) and social network analysis, etc. According to the resource dependency theory, it was discovered that community colleges and community development associations are influenced by tripartite resources such as community empowerment, lifelong learning, and social citizens in terms of the importance of resources, and of these three, lifelong learning accounts for a majority. In terms of resource ownership control, community colleges can provide community development associations with administrative support and assistance. In terms of degree of resource substitution, community colleges and community development associations are influenced by factors such as principal attitudes, environmental change, appearance of alternative institutions, etc.

It is frequently faced unforeseen managerial problems when users start to use the IT. Once the problem is identified, it is necessary to ensure an adaptation existed among the technology, the organization, and groups. The appropriateness of such adaptation even plays a critical role in successful IT implementation projects. The original equipment manufacturer (OEM) industry has been the most representative industrial model in Taiwan. This research found that during the use of Product lifecycle management (PLM) software system in Taiwan for supporting research and development (R&D) projects, the degree of adaptation depends on the interaction condition and appropriation among the technology, the organization, and groups. In the process of adaptation, discrepant events are resulted from the appropriation of technology. The degree of solving these discrepant events is a critical factor in determining whether the enterprise is able to gain competitive advantages from using an IT.

This study investigated how a Community Language Learning (CLL) approach, when utilised via new technology with the social network Facebook, can be most effective in a flipped EFL classroom. Curran [2] claims his CLL approach can reduce learner anxiety and insecurity. One feature of CLL is that trusting relationships are established not only between students and teachers but also among students themselves, and a learning community based on trusting relationships is claimed as the key element for the successful learning of foreign languages. Meanwhile, computer and mobile technologies have grown increasingly influential in many areas, including education. Flipped learning, a learning model first proposed by Sams and Bergmann [11], is becoming widespread globally, and mobile technology makes flipped classrooms both feasible and moveable. Liao and Fu’s study [14] showed that task rehearsal in computer-mediated communication does enhance learners’ performances. Thus, this study focused on learner anxiety and language performance issues.

By selecting mobile social networking sites as the research focus, this study examines perceived values that affect the behavioral intention of users to use mobile social networking sites continuously. Exactly how external variables are considered as internal/psychological values is also addressed to provide further insight into the interactions between external variables and the internally perceived values as well as the extent to which such interactions impact behavioral intention to continuously use. Results of this study significantly contribute to the efforts of social network developers and researchers to more thoroughly understand mobile social network users.

Social Network Service (SNS) has affected large sections of society. After Web 2.0 appeared, SNS caused a revolution in information flow with explosive diffusion. Many organizations have invested millions in their current Knowledge Management Systems (KMS). However, some limitations to KMS usage exist. Retrieval of optimal information and knowledge from repository systems can be difficult. This leads to avoidance of knowledge sharing and hinders knowledge transfer. This article demonstrates how horizontal communication structure and information diffusion created by SNS can affect the knowledge transfer mechanism. Our case study targets a representative IT-service enterprise in Korea. Our analysis reveals that SNS could provide a complementary technology for knowledge transfer activation because it affects organizational structure and cultural flexibility. However, SNS cannot substitute for KMS. This article provides a theoretical and experiential foundation for future research on the relationship between SNS and knowledge management.

The goal of the present study is to analyze the evolution of adolescent friendship network and depression. A network survey was carried out in classrooms of high schools. The participants are 93 boys and 82 girls. Sociometric data were collected by having each student nominate up to 16 intimate classmates. Mandarin Chinese version of the center for epidemiological studies-depression scale (MC-CES-D) was adopted as the measurement of depression. Panel data was collected across 3 semesters from Sep. 2008 to Jan. 2010. The program SIENA was applied to estimate the models for the evolution of social networks and depression. The result showed that gender had effect in the beginning, and depression had effect during the 2

nd

semester. The results and implication are discussed.

Due to the interest by public audience and academic research, there has been a great interest in Terrorist Networks by the academicians, analysts and criminologists. Either to learn how to disrupt or to prevent their activities, structure of these networks are investigated. The final conclusion about their structure and topology came to the fact that they do not resemble each other, but there are categories of them. In this paper, we categorized these networks into six because of their ideologies and common practices. Topologies of these six categories are observed and importance of key players (leaders, financiers, propaganda units and armed units) are compared based on these categories.

With the rapid growth of users in social networking websites, large amount of data are aggregated. Users are tending to find information through their friends on social network such as Facebook, and this behavior leads to a new search paradigm called social search. However, the traditional search engine like Google cannot handle this kind of search. The data cannot be indexed because of the membership privacy setting and social network relationships. Under this situation, it is harder and harder for users to search information related to their social network.

In this paper, we therefore proposed a system architecture which can deal with this issue and using the data from Facebook as example. An algorithm is also proposed which is the core technique of the system which is called Topic Participation Algorithm (TPA). Furthermore, we will propose a novel implemented social search engine which is developed based on the concept of social network analysis, data mining techniques and searching techniques.

According to multi-site relationship, this work presents a new issue named online multi-site utility mining, which considers not only quantities and profits of items in transactions but also online mining in a multi-site environment, to effectively address the distributed utility mining in multiple sites. In addition, an effective online framework, namely

TP-OMU

(Three-Phase Online Multi-site Utility mining algorithm), is proposed for coping with this problem, and the predicting strategy is designed to reduce the number of unpromising candidates by their utility upper-bounds in mining. Finally, the experimental results show

TP-OMU

has good efficiency in comparison with the traditional two-phase utility mining approach.

In this study, may reveal data and history of class selection of up to university graduation of students, clubs belong, such as from high school, or if it is being given what effect the career development of students which is the object. To reveal what the item is whether they affect the employment using the Decision Making Trial and Evaluation Laboratory method from student data.

In recent years, the concept of human-in-the-loop has been utilized to support environment sensing. Along with

IoT

(

Internet of Things

) and wearable computing technologies, connecting people and devices to the internet provides a significant advantage for real-time emergency management. For development of context-aware applications, it is important to utilize higher-level semantic information, such as human activity, social emotions, and human behaviors for event monitoring. Therefore, human users may become part of sensor networks by using mobile devices and social media to report local information around them. In this work, we mainly focus on the use of social messages spreading by human users to model the real-world events, in order to incorporate human sensors into event contexts for situational awareness. First, our algorithm computes the energy of each collected event messages, and then encapsulates ranked temporal, spatial and topical keywords into a structured node, which could reinforce the alert collected from physical nodes. The experimental results show that the proposed approach is able to extract essential entities of events for incorporating human sensors into event contexts for event prevention and risk management.

The influence degree of academic authors can be judged in one way by citation-based indices such as the h-index [1]. Marketing researchers develop a theory of action propagation to measure consumers’ influence in a market; by going viral, an action of marketing essential such as product adoption or brand awareness is spread to the majority of a market autonomously. Selecting the most influential consumers as marketing seeds is a hot topic in marketing research. In this study, we mine the most influential authors from the perspective of their ability to propagate scholastic actions such as attending a serial conference or publishing in a specific journal. The credit distribution model from Goyal et al. [2] is chosen as the propagation model with two academic publication networks (citation and coauthoring). Real data consisting of 10 years publication records from DBLP and ACM were used in the experiments. It is found that the citation network is more efficient than the coauthoring network to propagate scholastic actions. Top influential authors who can effectively affect fellows to attend a serial conference or publish in a specific journal are mined with the citation publication network.

.

The presently available methods are highly capable of offering personalized recommendations to individuals and also group recommendations to the subscriber hub. Operators who generate recommendations expect the information to be dissipated to a large number of subscribers in a specific space and time. In this paper, our interest is to combine the concepts of recommendations and information dissipation to identify the unique role players who can spread information rapidly with more specific to mobile telecom communities. It generates two improvements: reduces the extra cost incurred by the service provider in advertising to the entire community and also establishes a spatio-temporal mechanism to understand the sphere of influence of users in a dynamic environment. This is done by considering some essential factors to assign weights to each user in terms of the amount of influence they can exert within the group. Recent studies indicate that the feedback of a campaign is higher when recommended by peers rather than by operators. Our proposed method has another advantage that it can easily be extended to social network communities for similar purposes. We have also looked into the roles played by the individuals for different recommendations and the significance of the different role players’ impact on the community with the help of domain experts. Finally, we have evaluated our proposed method on mobile telecom data and compared with customer ranking algorithm statistically through paired t-test method.

The information visualization of networks has been a tricky task during the last decade. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. By examining some dynamics of criminal networks and by making use of some novel interactive features, we have found that the prevailing challenges to information visualization can be eliminated to a large extent. The current study will help understand interesting patterns, which are extracted by way of monitoring the temporal data of a criminal activity. We have appended six more features to the PEVNET framework. These are ‘Node color feature’, ‘Link size feature’, ‘Link details on demand feature’, ‘Detecting collaborating sub-cluster feature’, ‘Sub-cluster detection feature’, and ‘Temporal pattern feature’. A novel clustering algorithm has been proposed. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data.

In order to meet the increasing functional requirements of vehicular multimedia network video transmission, and improve the process of mobile video communication quality and the transmission bandwidth during the E-Commerce times. This paper proposed a center registration video transmission network on social mobile network which can solve the communication problems on such as mobile video conferencing, mobile office and so on. Through analyzing the design philosophy of vehicle-based central registration video transmission network node, at the same time discuss the realization of processing the video data during the social mobile communication under the center registration mechanism in the administration of media oriented system transport network. We use MOST150 video transmission node build a MOST150 video transmission network and via the output result which shows on the screen to prove the video transmission principle and the central registration mechanism of the MOST150 network. Experimental results show that the vehicle-based central registration video transmission network network based on center registration mechanism is propitious to the transmission of video data on social mobile communication network.

In this research, we propose an exercise support robotic system. The system collects human daily life activity data by motion detection as a part of big data, and analyzes it for providing better service. The Normal exercise tools such as pedometer need to be carried while exercising. However, sometimes people forget to take such exercise sensor, when it happened many times, people will feel hard to continue exercise regularly. Thus, the proposed system focuses on user’s behavior when exercise by Kinect, and based on it to recommends user a suitable exercise pattern through an interaction robot. Firstly, this paper discusses the user’s exercise habits and their weariness in exercise, secondly, describes the proposed system approach, the method of detecting human motion, and maintaining exercise motivation. Finally, the experiment and the results are shown.

The aim of this study is to discuss the effect of intervention on job satisfaction, professional commitment and social networks. A quasi-experimental design was used in which one pre-intervention survey and one post-intervention survey was used to collect data from the 20 nurses in a ward. The score of job satisfaction in the domain of human relationship and professional commitment were significantly improved. The other 4 domains of job satisfaction had not been changed. The advice network centralization was decreased after intervention, but not friendship network. The symmetric dyad of advice network was no longer affected by position after intervention. It is concluded that empowerment intervention may be used to increase professional commitment, but not job satisfaction.

For the past decade, the advanced development of Online Social Network (OSN) has demonstrated a considerable contribution to the industries and society. The current research proposes and examines the research models that incorporate types of emotion and trust as the antecedents of OSN adoption to describe individual benefits (IB) for positive emotion (PE) and negative emotion (NE) groups. A salient consideration is to examine the moderating effects of gender and involvement on the relationships between independent and dependent variables for both groups. Based on the analysis of 522 valid samples, research results show that (1) for PE group, attentive and active show significant effects on OSN adoption, respectively. (2) for NE group, only ashamed presents a significant influence on OSN adoption. (3) moderators of PE group report that alert, attentive, and active are significant, indicating that female reveals significant effects while male reveals insignificant effects for both alert and attentive, and low involvement reveals significant effects for inspired and active while high involvement reveals significant effects for attentive and active. (4) moderators of NE group report that upset, ashamed, and afraid are significant, indicating that female reveals a significant effect for upset while male reveals a significant effect for ashamed, and high involvement shows significant effects for both ashamed and afraid. Discussion and implications are also addressed.

Cloud computing is one of the most cutting-edge advanced technologies around the world. According to the recent research, many CIOs who work for famous corporation mentioned that security issues have been the most critical obstacles in the adoption of cloud technology. As one of the prominent application of cloud computing, cloud storage has attracted more concerns; however, many security problems existing in cloud storage need to be resolved. This applied research paper briefly analyzes the development of cloud storage security, comprehensive discussion of several general solutions to those problems introducing several non-technical issues, such as third-party issues, trust mechanism in cloud computing. Finally, some possible improvement to those solutions is provided.

Social media plays an immensely important role in business nowadays. With the social media platform and interactions continuously growing, an increasing number of organizations are engaging into it. The idea of collaboration via social networks is also a growing trend. Although social media benefits the companies, it also poses certain threats. Companies have the likelihood facing privacy invasion as well as encountering leakage of confidential and sensitive data. As a result, firms tend to bear a reputational risk within this social media era which is undoubtedly caused by the lack of information security.

This paper discusses the information security risks caused towards social media. It examines three important aspects: privacy, data leakage and human factors. The study uses interviews and surveys as research instruments in order to prove that these issues are critical and overlooked. In the discussion part, it highlights the reasons for which the aforementioned concerns are generated by the companies and the existing problems. Furthermore, in order to ensure information security and to eliminate the risks, a management strategy containing policy, SETA (security education, training and awareness) and technical control are proposed.

Open data has emerged to be a hot topic recently and attracted lots of attention from both researchers and practitioners. Many governments around the world are publishing government open data for public well-being, information disclosure, service innovation, and so on. Enormous amount of data and applications have been released and built in recent years. In accordance to such trend, authorities of Taiwan government are also striving to establish platforms for users to access and employ open data. However, the status and usage of these platforms are still unclear. This study conducts an exploratory investigation on the open data platforms in Taiwan to reveal their feasibility. Besides, we also try to assess the data quality of these platforms by examining medical facilities datasets published in these platforms. We hope that this research can provide a step stone for building better open datasets as well as platforms in Taiwan.

The teacher is requested to do a high-quality lecture to the solution of the issue of decline in academic ability. It is difficult to understand for the teacher the student’s achievement level. Therefore, there is a difference of understanding between the teacher and the student. Then, to understand student’s achievement level, we execute the questionnaire. And, we discuss the method of making the best use of the result from the analysis for the lecture improvement. As a result, the teacher can control the degree of progress of the class. Thereby, it is thought that the student can deepen understanding of the learning more. Furthermore, it is thought that we can expect the improvement of the achievement level by sending a student having a high achievement level of the group if I can constitute the social network of the friendly relations of the student.

As smartphones are widely available, automatically generated data through smartphones gain more importance. They attract large attention as the major source of big data. However, traditional survey data do not lose the value because it is essential for obtaining responses that reflect one’s thinking process and responding behaviors. Thus, online survey, which uses smartphones as input device, have become major method. Online survey is expected to have the benefits related to both size and thinking process. However, because of its nature of easy-to-use, the data quality may not be as good as paper-based survey. This paper looks into a case of the Big Five Personality Tests, which were run on Japanese university students, comparing paper-based and online-based/smartphone-based methods. Statistical analysis shows that there are differences between them. This paper suggests that the Big Five Personal Traits Test can be a yardstick to check the robustness of smartphone-based online survey.

In this paper we address the issue of privacy in Online Social Networks (OSN), focusing on those that use environmental sound as a contextual cue for users activity. Through the use of a costume-made research tool consisting of an Online Sound-Based Social Network (OSBSN), we undertook scientific experiments aiming to assess how users deal with the use of sound. Results show that contextual sound is regarded as important and useful for OSN but raises important privacy concerns. In order to deal with this constraint, we propose a system based on the automatic classification of sound environment rather than capturing and sharing actual audio.

The purpose of this study was to explore the effect of using Facebook after class in college learning and whether it is a good teaching tool. The ten-week educational discussions among fifty Taiwanese college students on two Facebook discussion pages were recorded and analyzed. The researchers examined the relationship between Facebook Group participation and the academic achievement of college students. Results showed that there was a significant positive correlation between a student’s online participation frequency and his GPA. The frequency of student participation in Facebook Groups could explain 15% to 19% of the variance in the grades they received for their exhibition projects. Furthermore, it was found that evaluation criteria designed by instructors can have an effect on the direction of online discussions. According to the results, we can conclude that educational discussions on Facebook can be beneficial to college students’ learning. However, whether Facebook can serve as an effective teaching tool depends on how the educators use it in students’ learning process.

Input-Output table is important for analyzing the relationship among industrial sectors when we decide a policy making. In this paper we propose to consider the indirect effect between sectors in sales and sectors in purchases from Input-Output table. We adopt a economical interaction model permitting consideration of several path selection for each origin-destination pair. The economical interaction model is considered as a general model because it has been developed by entropy maximization principle. From observed data about total output in sales and purchases, our model can estimate interdependencies between different industrial sectors. The parameter estimation procedure for the proposed model is developed.

In this paper, we study the load capacity of e-government system and try to find the parts that can be optimized. In order to increase the load capacity of the server, we introduce Hiphop-PHP technology into the e-government system that was developed by php language. By using Hiphop-PHP program, PHP language can be converted into compiled C++ code. It can significantly reduce the computing time of server and the load pressure of the web server. At the same time, this paper also proposes other ways to improve server performance based on Hiphop-PHP technology. It combines the advanced cache reading technology, memory management technology and Hiphop-PHP technology together to improve the load capacity of the server. The simulation uses apache http server benchmarking tool that was developed by the apache software foundation to test the server. The result shows that the system can face up to the worst case of user requests. The introduction of Hiphop-PHP technology can significantly improve the load capacity of the server.

This paper attempts to enhance the learning performance of radial basis function neural network (RBFN) through swarm intelligence methods and self-organizing map (SOM) neural network (SOMnet). Further, the particle swarm optimization (PSO) and genetic algorithm (GA)-based method (i.e., PG approach) is employed to train RBFN. The proposed SOMnet + PG approach (called: SPG) algorithm combines the automatically clustering ability of SOMnet with PG approach. The simulation results revealed that SOMnet, PSO, and GA methods can be integrated ingeniously and redeveloped into a hybrid algorithm which aims for obtaining the best accurate learning performance among other algorithms in this study. On the other hand, method evaluation results for two benchmark problems and a gold price prediction case showed that the proposed SPG algorithm outperforms other algorithms and the auto-regressive integrated moving average (ARIMA) models in accuracy.

Among various data mining techniques, sequential-pattern mining is used to discover the frequent subsequences from a sequence database. Most research handles the static database in batch mode to discover the desired sequential patterns. Transactions or customer sequences are, however, dynamically changed in real-world applications. In the past, the FUSP tree was designed to maintain and update the discovered information based on Fast UPdated (FUP) approach with sequence insertion and sequence deletion. The original customer sequences is still required to be rescanned if it is necessary. In this paper, the prelarge concept is adopted to maintain and update the built FUSP tree with sequence deletion. When the number of deleted customers is smaller than the safety bound of the prelarge concept, the original database is unnecessary to be rescanned but the sequential patterns can still be actually maintained and updated. Experiments are also conducted to show the performance of the proposed algorithm in terms of execution time and number of tree nodes.

This paper proposes a novel reversible data hidng scheme in images compressed by absolute moment block truncation coding (AMBTC). In this scheme, the secret data is embedded into the quantization levels of each AMBTC-compressed image block based on a reference matrix. Original quantization levels are transformed into another watermarking message which will combine with bitmap in each image block. The reconstructed image quality is exactly the same as the original AMBTC-compressed version due to the reversibility. Extensive experimental results demonstrate the effectiveness of the proposed scheme and good image quality of the embedded image.

Time-bound hierarchical key assignment (TBHKA) scheme is a cryptographic method to assign encryption keys to a set of security classes in a partially ordered hierarchy. Only the authorized subscriber who holds the corresponding key can access the encrypted resources. In 2005, Yeh proposed a RSA-based TBHKA scheme which is suitable for discrete time period. However, it had been proved insecure against colluding attacks. Up to now, no such TBHKA schemes were proposed. In this paper, we fuse pairing-based cryptography and RSA key construction to propose a secure TBHKA scheme. In particular, our scheme is suitable for discrete time period. The security analysis is demonstrated that our scheme is secure against outsider and insider attacks (including colluding attacks). Finally, the performance analysis and comparisons are given to demonstrate our advantage.

Automated scoring technology aims to reduce the workload of homework or examinations and to ensure the fairness. Therefore, to study automated scoring technology and its implementation process has great practical meaning. A method of automated assessment of subjective tests based on domain ontology and corpus is proposed to solve the existence problems of automated scoring systems. The conception of domain ontology is introduced and a software engineering domain is built constructed. Some key technologies includes Chinese word segmentation, the TF-IDF algorithm which is utilized to calculate the importance of each keyword in texts and text similarity calculation are described in detail. The method mentioned in the paper has been applied in the automated assessment of short-answer questions of software engineering. Comparison between the results made by the automatic scoring system and the teachers proves reasonableness of the model.