在线社会网络的动态社区发现及演化

在线社会网络研究中,动态隐含社区或群组结构的发现及演化探测是一个十分关键的核心问题,它对于在中观(Mesoscopic)视图观察在线社会网络隐结构特征、预测演化趋势、掌控网络势态、发现网络异常群体事件等具有重要意义.文中首先分析了动态社区发现和社区演化研究的关系,给出动态社区研究中关键挑战问题;然后根据问题背景的不同,从“同构社会网络的动态社区研究”和“异构社会网络的动态社区研究”两个方面进行国内外相关研究现状的阐述和分析,其中,在“同构社会网络的动态社区研究”中,根据评价方法的差异和关注问题的不同将当前相关研究分为基于时空独立评价、时空集成评价、统一评价和增量式算法4大类进行综述,同时对动态社区发现的重要应用——异常群体发现的研究进行介绍;最后对在线社会网络动态社区领域的难点和发展趋势进行分析和展望.     

I Know My Professor: Teacher Self-Disclosure in Online Education and a Mediating Role of Social Presence

Acknowledging the importance of teacher–student relationship for effective learning experiences, the present study examined the role of teacher self-disclosure and social presence in online education. An online survey was conducted from a sample of 262 undergraduate students with online class experiences. The findings suggest that students’ perception toward teacher self-disclosure increased students’ feeling of social presence about their teacher. Then, social presence in turn led to teacher–student relationship satisfaction, which ultimately increased perceived knowledge gain. Importantly, the association between teacher self-disclosure and teacher–student relationship satisfaction was mediated by social presence.     

Models of Influence in Online Social Networks

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users' behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users' behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends' behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.     

在线社交网络的信息传播

定位和溯源是控制信息传播的基础,理解和研究信息传播过程则是溯源的前提.在线社交网络(online social network,OSN)是网络生活的重要内容,用户在在线社交语境中进行的一系列活动从本质上来讲都是在进行信息交互,针对这一语境下信息扩散的特征,综合现有研究中的传染病传播模型的设计思路,基于CSR模型,提出一种新型的模型CSRR,为在线社交网络的进一步分析提供重要的参考和依据,同时为公安机关的侦查工作和舆情监管提供技术支持.     

Competition and Information Deception in Online Social Networks

This study empirically examines the role of competition in determining intentions toward personal information deception (PID) among users of online social network (OSN) sites. PID refers to OSN users intentionally misrepresenting or refusing to disclose online personal information. The research proposes that consumers’ intentions toward PID depend on their desire for online competition with other OSN users, which in turn depends on user appraisals of available status and hedonic benefits, as well as established social norms around competition. An analysis of data gathered from 499 OSN participants (students enrolled at a state university in the southeastern United States) shows that competitive desires represent an important antecedent of PID behavior in OSN contexts. Theoretical and practical implications of the research are also discussed.     

The Effects of Social Influence on User Acceptance of Online Social Networks

With the proliferation of online social networks, understanding how and why individuals adopt and use these networks can help managers and marketers to design better methods and approaches toward engaging their users. The purpose of this study is to investigate the determinants of user acceptance of online social networks, with particular attention given to the effects of social influence. A research model was developed by incorporating two variables of social influence, subjective norm and critical mass, into an enhanced version of the Technology Acceptance Model, specifically to address issues related to online social networks. The model was empirically evaluated using survey data collected from 269 subjects about their perceptions of online social networks. The results reveal that both subjective norm and critical mass significantly affect perceived usefulness, which further affects users' usage intention, and perceived ease of use affects the usage intention indirectly through perceived usefulness. The implications of this study on theory and practice are discussed.     

Organization Mining Using Online Social Networks

Complementing the formal organizational structure of a business are the informal connections among employees. These relationships help identify knowledge hubs, working groups, and shortcuts through the organizational structure. They carry valuable information on how a company functions de facto. In the past, eliciting the informal social networks within an organization was challenging; today they are reflected by friendship relationships in online social networks. In this paper we analyze several commercial organizations by mining data which their employees have exposed on Facebook, LinkedIn, and other publicly available sources. Using a web crawler designed for this purpose, we extract a network of informal social relationships among employees of targeted organizations. Our results show that it is possible to identify leadership roles within the organization solely by using centrality analysis and machine learning techniques applied to the informal relationship network structure. Valuable non-trivial insights can also be gained by clustering an organization’s social network and gathering publicly available information on the employees within each cluster. Knowledge of the network of informal relationships may be a major asset or might be a significant threat to the underlying organization.     

在线社交网络中的新兴话题检测技术综述

新兴话题检测是社交网络研究的热点问题之一。在线社交网络特别是微博的开放性,给话题的流行和爆发提供了前所未有的便利条件。新兴话题是即将流行或爆发的话题,往往伴随着重大的事件或新闻的发生,会产生重大的社会影响,如何在早期识别此类话题,是新兴话题检测研究的主要内容。该文回顾了近年来在新兴话题检测方面的主要进展,分析了新兴话题检测领域面临的挑战,阐述了相关的概念、方法和理论,重点从内容突发特征和信息传播模型两个方面对影响新兴话题检测的方法进行了分析和讨论,并对新兴话题检测的前景做了展望。
     

The Perils and Promises of Self-Disclosure on Social Media

In addition to their professional social media accounts, individuals are increasingly using their personal profiles and casual posts to communicate their identities to work colleagues. They do this in order to ‘stand out from the crowd’ and to signal attributes that are difficult to showcase explicitly in a work setting. Existing studies have tended to treat personal posts viewed in a professional context as a problem, since they can threaten impression management efforts. These accounts focus on the attempts of individuals to separate their life domains on social media. In contrast, we present the narratives of professional IT workers in India who intentionally disrupt the boundaries between personal and professional profiles in order to get noticed by their employers. Drawing on the dramaturgical vocabulary of Goffman (1959) we shed light on how individuals cope with increased levels of self-disclosure on social media. We argue that their self-presentations can be likened to post-modern performances in which the traditional boundaries between actor and audience are intentionally unsettled. These casual posts communicate additional personal traits that are not otherwise included in professional presentations. Since there are no strict boundaries between formal front-stage and relaxed back-stage regions in these types of performance, a liminal mental state is often used, which enables a better assessment of the type of information to present on social media.     

基于在线社会网络的用户影响力研究

对大规模的在线社会网络图结构进行了较为系统的分析,结果表明社会网络的入度、出度、发文数等基本符合幂律分布。社会网络的小世界属性也使得强连通关系呈现“纺锤体”形状。该文从用户的阅读概率角度引入用户的发文行为、浏览行为与标签社区小世界属性等对用户的社会影响力模型进行建模。实验结果显示PTIM模型融合了发文行为与小世界属性等特性,在最具影响力用户节点、用户粉丝数、认证用户数与人工标注的相对用户影响力大小等指标上均表现出稳定的性能。     

The Impact of Geographic Distance on Online Social Interactions

Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms.     

Mining Trust Relationships from Online Social Networks

With the growing popularity of online social network,trust plays a more and more important role in connecting people to each other.We rely on our personal trust to accept recommendations,to make purchase decisions and to select transaction partners in the online community.Therefore,how to obtain trust relationships through mining online social networks becomes an important research topic.There are several shortcomings of existing trust mining methods.First,trust is category-dependent.However,most of the methods overlook the category attribute of trust relationships,which leads to low accuracy in trust calculation.Second,since the data in online social networks cannot be understood and processed by machines directly,traditional mining methods require much human effort and are not easily applied to other applications.To solve the above problems,we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically.We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships.We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort.We perform experiments on real-life data extracted from Epinions.The experimental results verify the effectiveness and wide application use of our proposed method.     

Relationship Identification Across Heterogeneous Online Social Networks

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.     

Tweeting to Feel Connected: A Model for Social Connectedness in Online Social Networks

Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, a theoretical model of social connectedness based on the functional and structural characteristics of people's communication behavior within an online social network is developed and tested. The study investigates how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, the study looks at the concurrent direct and moderating effect of two structural constructs characterizing people's online social network: network size and frequency of usage. The research model is tested using data (n?=?121) collected from two sources: (a) an online survey of Twitter users and (b) their usage data collected directly from Twitter. Results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, whereas network size has a moderating effect. Social presence is found to partially mediate the relationship between social awareness and social connectedness. The findings of the analysis are used to outline design implications for online social networks from a human–computer interaction perspective.     

社会道德风险在计算机网络中的影响

随着信息技术的发展,计算机网络在人们生活中的应用越来越广泛,但是随着计算机网络的逐渐普及和应用,社会道德风险逐渐体现出来.本文对于社会道德风险在计算机网络中的影响作了简要的分析.     

在线社交网络中群体影响力的建模与分析

移动互联网技术的飞速发展,给社交网络平台带来了新的颠覆性的转变,也不断地改变着人们的生产、生活和交流方式.在线社交网络由于其特有的注册开放性、发布信息自由性、用户兴趣趋同性等特点,已经超越传统媒体,成为人们传播消息、获取新闻和接收实时信息的主要途径.同时,社交网络中用户之间的各种关系类型多样、相互交织、相互影响,促使用...     

Not Even Past: Information Aging and Temporal Privacy in Online Social Networks

Online social networks (OSNs) make information accessible for unlimited periods and provide easy access to past information by arranging information in time lines or by providing sophisticated search mechanisms. Despite increased concerns over the privacy threat that is posed by digital memory, there is little knowledge about retrospective privacy: the extent to which the age of the exposed information affects sharing preferences. In this article, we investigate how information aging impacts users’ sharing preferences on Facebook. Our findings are based on a between-subjects experiment (n = 272), in which we measured the impact of time since first publishing an OSN post on its sharing preferences. Our results quantify how willingness to share is lower for older Facebook posts and show that older posts have lower relevancy to the user’s social network and are less representative of the user’s identity. We show that changes in the user’s social circles, the occurrence of significant life changes and a user’s young age are correlated with a further decrease in the willingness to keep sharing past information. We discuss our findings by juxtaposing digital memory theories and privacy theories and suggest a vision for mechanisms that can help users manage longitudinal privacy.     

A Privacy-Preserving Attribute-Based Reputation System in Online Social Networks

Online social networks (OSNs) have revolutionarily changed the way people connect with each other. One of the main factors that help achieve this success is reputation systems that enable OSN users to ...