社会网络应用中数据迁移的隐私风险模式研究

由于在社会网络应用程序站点上自愿添加个人身份信息的人数不断增加,站点服务商可以从中获益,但是同时数据误用的风险会威胁个人用户的隐私信息以及服务商的商业模式。本文根据最近的调查,分析了开发隐私保护社会网络应用的主要需求,在此基础上提出了隐私风险模式,以增强数据或社会网络迁移过程中的信息隐私保护。通过确定与个人身份信息迁移过程中重要的问题设计出了该隐私风险模式。     

Privacy in social networks: An analysis of Facebook

The only way to be aware of the risks and threats of Facebook, the most commonly used social networking site in the world and Turkey, is to be a careful user changing the default settings or simply not to have a Facebook account. In Turkey, there is still no study in which personal information shared though social networking sites has been evaluated in terms of privacy. For this reason, the findings obtained of this study have a great importance in the general picture of the current situation and drawing attention to the risks of the issue in Turkey where there are no legal arrangements effectively protecting the users from such sites. This study aims to investigate the Facebook privacy of information professionals who are members of KUTUP-L, and to determine the sensitivity and level of awareness of information professionals in Turkey. Facebook user profiles of 400 information professionals, all KUTUP-L members, have been analyzed in a study examining 32 different privacy settings. A privacy score has been calculated for each user, and the relations between privacy results have been analyzed. The findings at the end of the study show that information professionals in Turkey do pay attention to privacy, and most of the users change the default settings in order to protect their personal information.     

面向分布式数据挖掘的隐私保护研究

在不泄露隐私信息或敏感知识的基础下,如何挖掘出分布式数据中准确的规则信息,已成为数据挖掘领域的重要研究方向之一。本文总结了面向分布式数据挖掘的隐私保护技术的现状,并比较分析了不同方法的优缺点,另外还提出了面向数据挖掘隐私保护技术的性能评估标准,展望了未来进一步的研究方向。     

基于共享的隐私保护关联规则挖掘

随着数据挖掘技术的广泛使用,产生了信息安全和隐私保护的新问题。对当前分布式隐私保护关联规则挖掘的经典算法进行了改进,在不使用当前流行的多方安全计算(SMC)的条件下,用较简单的方法进行隐私保护关联规则挖掘,降低了运算量。同时,在分布式关联规则挖掘的同时,很好地保持了各个站点的数据和信息。     

Exploring factors affecting social e-commerce service adoption: The case of Facebook Gifts

Although social commerce is an important trend in practice, relatively few research studies have explored the impact of social commerce innovations launched within social networking sites. The deployment of a gift-giving service within a social networking site provides a unique opportunity to study the intersection of technological innovations and social norms and its potential to generate new revenue for ecommerce sites. Using Facebook Gifts as a real-world context for the study, we explore the factor structure of salient user beliefs influencing usage intention, and examine the relationships between beliefs and intention to use the service in a broader nomological network. Instead of adopting constructs from existing models, we started with the elicitation of salient beliefs and proceeded with successive stages of refinement to develop a suitable model. The empirical results show countervailing effects of perceived social utility and perceived convenience of the service on one hand, and low perceived value and privacy concerns on the other. A notable finding is the potential conflict arising between the expectations of effort associated with the procurement of a gift and the common perception of technology as reducing the required effort. The net result is that a technology-mediated gift service in the context of a social networking site runs counter to the social norms associated with traditional gift exchanges. This insight provides evidence of the dual challenge for social commerce initiatives. In order to be successful, new services in this area must leverage the potential of the technology as well as social practices.     

A big data analytics framework for detecting user-level depression from social networks

Depression is one of the most common mental health problems worldwide. The diagnosis of depression is usually done by clinicians based on mental status questionnaires and patient's self-reporting. Not only do these methods highly depend on the current mood of the patient, but also people who experience mental illness are often reluctantly seeking help. Social networks have become a popular platform for people to express their feelings and thoughts with friends and family. With the substantial amount of data in social networks, there is an opportunity to try designing novel frameworks to identify those at risk of depression. Moreover, such frameworks can provide clinicians and hospitals with deeper insights about depressive behavioral patterns, thereby improving diagnostic process. In this paper, we propose a big data analytics framework to detect depression for users of social networks. In addition to syntactic and syntax features, it focuses on pragmatic features toward modeling the intention of users. User intention represents the true motivation behind social network behaviors. Moreover, since the behaviors of user's friends in the network are believed to have an influence on the user, the framework also models the influence of friends on the user's mental states. We evaluate the performance of the proposed framework on a massive real dataset obtained from Facebook and show that the framework outperforms existing methods for diagnosing user-level depression in social networks.     

SocConnect: A personalized social network aggregator and recommender

Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends’ social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends’ updates and other activities). To address these two problems, we propose a user-centric system called “SocConnect” (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends’ activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends’ activities and help users deal with cognitive overload.     

Privacy concerns and disclosure of biometric and behavioral data for travel

In light of mounting privacy concerns over the increasing collection and use of biometric and behavioral information for travel facilitation, this study examines travelers’ online privacy concerns (TOPC) and its impact on willingness to share data with travel providers. A proposed theoretical model explaining antecedents and outcomes of TOPC related to biometric and behavioral data sharing was tested using structural equation modeling with data collected from 685 travelers. The results extend the Antecedents – Privacy Concerns – Outcomes (APCO) framework by identifying a set of salient individual factors that shape TOPC. The findings provide empirical evidence confirming the context dependence of privacy preferences, showing that although travelers are concerned over their information privacy they are still willing to share their behavioral data; while in the case of biometric information, the disclosure decision is dependent upon expected benefits rather than privacy concerns. This study offers insights into privacy behavior of online consumers in the travel context and constitutes one of the few focusing on the social aspects of biometric authentication.     

Building Guanxi network in the mobile social platform: A social capital perspective

Mobile social platform such as WeChat Moments has gained great popularity in China in the past few years. However, there are still a lack of studies that focus on Guanxi network building in the virtual social community. Drawing upon social capital theory and technology affordance theory, this study develops a research model to examine the influences of platform media richness and interactivity on users’ social capital and participation behaviors in the WeChat Moments. An empirical survey was conducted in China and 287 valid data were collected from WeChat users. Structural equation modelling analysis was used to test the research model. The empirical results suggest that platform interactivity and media richness are significant technology affordances that promote users’ Guanxi network, and their influences are mediated by social interaction and shared understanding. Furthermore, Guanxi network is positively associated with users’ active participation in the WeChat Moments. Theoretical and practical implications are illustrated in the final section.     

组织知识共享网络模型研究——基于知识网络和社会网络

 以组织中同时作为社会活动和知识活动载体的“人”为研究对象,以构建一个客观高效的组织知识共享网络模型为研究目的,提出基于知识网络和社会网络的组织知识共享网络模型及构建方法,并探讨该网络模型如何影响组织中主体间知识共享的过程和方式。从应用实例来看,该方法具有客观、可视化、定量等特点,因而可以较好地应用到组织知识的分布、查找、推送等知识共享实际应用中去,为组织知识共享的定量分析提供了一种新的工具和思路。     

A multilevel model for effects of social capital and knowledge sharing in knowledge-intensive work teams

Although it is a widely held belief that social capital facilitates knowledge sharing among individuals, there is little research that has deeply investigated the impacts of social capital at different levels on an individual's knowledge sharing behavior. To address this research gap, this study combines a multilevel approach and an optimal network configuration view to investigate the multilevel effects of social capital on individuals’ knowledge sharing in knowledge intensive work teams. This study makes a distinction between the social capital at the team-level and that of social capital at the individual level to examine their cross-level and direct effects on an individual's sharing of explicit and tacit knowledge. A survey involving 343 participants in 47 knowledge-intensive teams was conducted for testing the multilevel model. The results reveal that social capital at both levels jointly influences an individual's explicit and tacit knowledge sharing. Further, when individuals possess a moderate betweenness centrality and the whole team holds a moderate network density, team members’ knowledge sharing can be maximized. These findings offer a more comprehensive and precise understanding of the multilevel impacts of social capital on team members’ knowledge sharing behavior, thus contributing to the social capital theory, as well as knowledge management research and practices.     

在线社交网络中好友亲密度判定方法研究

与现实生活中的朋友关系不同,多数的在线社交网络系统为每个人只设定一个简单的二元好友联系,即要么是朋友,要么不是。这种好友关系的定义为个人隐私信息的保护带来了许多问题。分析了一种基于当前社交网络平台的互动信息自动判定好友亲密度方法,并结合该方法提出了一种半自动的隐私控制框架。     

Exploring individuals’ subjective well-being and loyalty towards social network sites from the perspective of network externalities: The Facebook case

This study aims to investigate users’ subjective well-being and loyalty towards social network sites (SNSs). Despite the growing role of network externalities in SNS continuance decisions, the SNS usage literature has paid scant attention to the relationship between network externalities, SNS identification, and users’ subjective well-being. In this study, we identify four components of network externalities: perceived network size, external prestige, compatibility, and complementarity. In the research model, both network size and external prestige are hypothesized positively to affect SNS identification. Perceived compatibility and perceived complementarity are hypothesized positively to affect user satisfaction. Satisfaction and SNS identification are hypothesized positively to affect user subjective well-being and loyalty towards the SNS. Users’ subjective well-being is hypothesized positively to affect their loyalty towards the SNS. Data collected from 615 valid users of Facebook provide strong support for most of these hypotheses. The findings indicate that perceived network size negatively affects users’ SNS identifications. Other components of network externalities have positive effects on SNS identification and satisfaction, which in turn have positive effects on users’ subjective well-being and loyalty towards SNS. Implications for theory and practice and suggestions for future research are also discussed.     

社交网站调查的科研数据一致性研究

社交网站为科研调查者提供了新的调查途径与平台,科研调查者可基于不同社交网站平台,在不同调查情景中实施科研调查以弥补单一调查模式样本涵盖率低的缺陷。论文以人人网、腾讯QQ为例,采用验证性因子分析法,研究了匿名调查与非匿名调查获取科研数据的一致性问题。研究结果表明:非敏感性量表下两类调查数据具备完全一致性,科研人员可将所得数据直接整合;对于敏感性量表,两类调查数据不具备完全一致性,科研人员只有对获取数据进行相关预处理后,才可进行数据整合及相关统计分析。     

数据论文联合出版模式及数据论文出版

【目的】 分析期刊编辑部与数据中心联合出版数据论文的模式,为进一步推进数据论文出版提供参考。【方法】 借鉴国际数据期刊论文出版模式,建立了期刊编辑部与数据中心联合出版数据论文的模式和流程,并分析了数据论文出版实践工作取得的成效和发展方向。【结果】 以期刊编辑部为主体,通过与数据中心联合,探索数据论文出版的流程,实现了数据论文的出版。【结论】 数据论文出版是一种新型论文形式,是对科学数据的肯定,能有效提高期刊的影响力。     

基于社会网络分析和密度算子的学术期刊综合评价研究

【目的】 针对学术期刊多属性综合评价中指标间相关性较强的问题,提出一种新的基于社会网络分析和密度算子的学术期刊综合评价模型。【方法】 将学术期刊评价指标视为网络节点,依据节点间相关性构造关系网络矩阵。设计节点中心度和子群凝聚度,度量节点和子群的重要性。基于密度算子综合集成评价信息,计算并且排序学术期刊综合评价值。【结果】 以图书情报类学术期刊为研究对象,实证结果表明该方法能够充分顾及指标间的相关性,得出较为合理的评价结果,是一种切实可行的学术期刊综合评价方法。【结论】 基于社会网络分析和密度算子的学术期刊综合评价模型为科研机构评价学术期刊提供了一种新思路。     

社会网络视角下的技术范式转换预见探讨

在当今技术变革加速的时代,一些企业尽管拥有强大的研发实力和主流市场的领导者地位,然而由于对技术发展趋势判断错误或者忽视新技术的市场潜力,一再错失重要发展机遇的情况时有发生。在此背景下,企业如能预见新的技术范式并识别、培育出突破性技术,则能成为新范式的掌舵者、行业的领导者。目前,已有的、基于线性思维的技术预测/预见方法尚难以解决包含高不确定性、复杂性的技术范式转换问题。文章认为,社会网络从一个网络的规模、密度、资源等角度,通过数据挖掘等工具和实证研究方法刻画范式转换的特征和演化路径,从而可能寻找到技术范式转换预见的途径。     

A social status perspective of network utility over electronic channels in academic communities

Early research on the social implications of e-mail has promoted the role of electronic communication channels in fostering social equality and reducing gaps between the social classes. Follow-up studies, however, suggest that people continue to rely on social cues in electronic communication as a way of dealing with uncertainties and reducing feelings of discomfort associated with unfamiliar contexts. Based on a review of the Social Cognitive Theory, social status and electronic communication literatures, along with the results of a previous qualitative study, we propose a formal model that indicates how self-perceptions of social status are related to the use of e-mail and the acquisition of social resources over electronic channels. The model is tested using data collected from 206 faculty members in a major U.S. university. The empirical results support the proposed model implying that self-perceptions of social status influence social assertiveness, which in turn reinforces the use of e-mail to seek help and acquire social resources from others. The use of e-mail for the acquisition of social resources is associated with a socially diverse network whose contacts are perceived to acquire valuable information and to extend instrumental support for career development. Implications are drawn for both theory and practice.