社交网络研究综述

社交网络已成为Web2.0时代最流行的应用,其服务范围已逐步从社交关系管理扩展到媒体信息、应用集成、电子商务等领域。社交网络中大量的活跃用户为研究网络行为、数据安全、信息传播以及其他跨学科问题提供了宝贵的数据和场景。自Facebook出现以来,研究者先后从不同的角度对社交网络进行了大量的研究,这些研究对人们认识社交网络内部规律、促进ICP服务改进具有重大意义。首先对社交网络的发展进行了简单的回顾;然后从社交网络的数据采集技术、社交网络用户行为分析、社交网络中的信息传播及社交网络中的用户隐私4个方面对已有的研究工作进行总结评价;最后,总结了当前研究中出现的问题并对未来研究发展趋势进行了展望。希望能为该领域的研究者提供一些有益的启示。     

在线社会网络用户的信息分享行为预测研究

在线社会网络中信息的传播路径包含着用户对内容、来源等的偏好信息,研究运用信息的传播路径来预测用户信息分享行为的方法。基于传播路径的信息过滤能力研究了信息在网络中的传播过程和信息传播路径的转换方法。运用基于关联规则的分类算法对在线社会网络中的信息分享行为进行预测。以新浪微博为例对微博用户的转发行为进行了预测,结果表明该方法对在线社会网络中的活跃用户的信息分享行为的预测具有较好的效果。     

Interplay between trust,information privacy concerns and behavioural intention of users on online social networks

Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trust, privacy concerns and behavioural intention of users on the social network. The behavioural intention of users on the online social network (OSN) is captured by intention to disclose information and intention to interact with others in OSN. The study was conducted on a sample of 457 active users from one of the major social networking website, Facebook. Partial least squares based structural equation modelling was used for analysing the results. The findings of the study reveal that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others. Another important finding of the study indicates that prior positive experience with the website significantly impacts the trust in website, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.     

面向社交网络的访问控制模型和策略研究进展

作为信息保护有效手段之一的访问控制技术,可以保障信息仅能被合法用户访问,防止信息的泄露,势必成为在线社交网络中信息保护方法的重要组成部分。通过分析在线社交网络的特点,从访问控制模型与访问控制策略2个角度出发,对目前在线社交网络中的访问控制研究进行深入探讨,并对相关研究进行整理与分析。     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

应用随机游走的社交网络用户分类方法

针对现有在线社交网络用户分类方法不能有效利用用户属性和关系网络信息提高分类性能的问题,设计了一种基于随机游走模型的多标签分类方法MLCMRW。该方法的分类过程包括学习用户初始化类别标签以及通过迭代推理获得用户稳定标签分布两个阶段,并且其可以同时考虑用户属性以及关系网络特征信息进行分类。多个在线社交网络数据集上进行的实验表明,MLCMRW比其它已有的代表性方法有更好的分类性能,并且更适合对现实中的在线社交网络进行用户分类。     

基于用户意图理解的社交网络跨媒体搜索与挖掘

随着在线社交网络的盛行,网络用户不仅对信息资讯的获取速度和实时性提出了更高的要求,对个性化和精确化的搜索需求日益增长。为了提升搜索引擎的质量以及其结果列表的准确性,需要深层次地挖掘用户搜索意图。本文分析了用户搜索意图理解在线社交网络跨媒体进行精准搜索与挖掘的研究现状,包括知识图谱在线社交网络多模态信息感知、面向用户搜索意图匹配的跨媒体大数据深度语义学习方面的应用,以及用户搜索意图理解的在线社交网络精准搜索与挖掘的应用等。最后,对未来研究存在的问题和可能面临的挑战进行了展望。     

Exploring the influence of personal schema on trust transfer and switching costs in brick-and-click bookstores

Due to competition, most firms try to retain and lock in customers; this is likely to ensure a greater market profit than that gained by acquiring new customers. While many studies have investigated customer satisfaction and retention in online and offline firms, scant research exists on customer satisfaction and customer retention in the context of brick-and-click firms: i.e., those that conduct business both online and in stores. Such firms encourage trust transfer among their customers. However, most studies have failed to consider the role of personal schema in trust transfer. Based on social exchange, investment, and information integration theories, we analyzed the impact of trust transfer, switching costs, and personal schema on customer satisfaction and repurchase intentions in the context of an offline bookstore that has now launched an online business. Our results indicated that trust transfer and switching costs had a small effect on customer satisfaction and repurchase intentions. Personal schema, however, affected both customer satisfaction and repurchase intention.     

Designing social commerce platforms based on consumers’ intentions

Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.     

Forming an intention to act on recommendations given via online social networks

Online social networks (OSN) are technology-enabled relationship tools in which a user creates a profile, connects to other individuals, and shares user-generated content with them. One of the many features OSN offer is the ability to post messages in the form of product and service recommendations. Although word-of-mouth research has examined this type of personal communication to intentionally influence consumer decisions, limited understanding exists regarding when a user acts upon a recommendation received from one of their contacts. In this study, we draw on relationship theories and research on trust to analyze the impact relationship characteristics and behaviors of the OSN contacts have on trust in the recommendation that subsequently leads a user to act on this OSN contact’s recommendation. The results of a survey of 116 OSN users showed that closeness, excessive posting behavior, and past recommendation experience have a positive impact on trust and intention to act on the recommendation. One characteristic of the relationships, that is, similarity, however, does not affect recommendation trust in the context of OSN. The findings enhance our understanding of relationships and their defining characteristics in OSN. The results also provide insights into how companies might leverage OSN in their marketing efforts.     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

Investigating user switching intention for mobile instant messaging application: Taking WeChat as an example

Post adoptive IT use is an important research topic in information systems field, mainly including sustained behaviours and switching behaviours. While there are a great number of studies on users’ continuance intentions for diversified IT, users’ IT switching behaviours are less studied. This research attempts to identify the features of users IT switching behaviours. We introduce a migration theory from social network perspective to explore the intrinsic and extrinsic factors influencing users’ switching intention in the context of mobile instant messaging (MIM) application. In particular, we develop a model that examines the role of networks, deprivations and trusts on MIM users’ switching intentions to WeChat in China. A survey research method is utilized to test this model and hypotheses. We found that functional deprivation, monetary deprivation and personal innovativeness could positively influence users’ switching intentions. Networks of obligation was found to have no significantly direct influence on switching intentions, but fully mediated by functional and monetary deprivations. However, trust transferred from MIMs provider has no significant effect on switching intentions. The findings are believed to theoretically contribute to further understand users’ IT switching behaviours and yield some practical implications for designers and managers in MIM providers and their products propaganda.     

How media choice affects learner interactions in distance learning classes

In this research we explore aspects of learning, social interaction and community across online learning, also known as distance learning, in higher education. We measure the impact of online social networking (OSN) software versus traditional learning management system (LMS) software. Guided by a theoretical model for how individuals learn and interact within online communities, we measure student perceptions of learning, social interaction and course community before and after our interventions. Survey instruments measure perceived learning, social interaction and community, which we further explore using social network analysis (SNA). Survey results identified that students experienced higher levels of perceived social interaction and course community and, overall, had higher levels of satisfaction with OSN software than those using LMS software. Along this line, SNA results corroborated that OSN software yielded a higher number of interactions, providing a more engaging learning experience.     

Novel authentication procedures for preventing unauthorized access in social networks

Online Social Network (OSN) is the most widely used platform to communicate with one another. It is fast and cheap when compared to other modes of communication. However, Online Social Network profiles are being hacked often by employing various types of malicious attacks. Most of the times the attackers are successful in accessing the OSN account which illustrates that the present authentication mechanisms are not efficient enough. Once an online social network account is being hacked, it can be misused widely and the hacker makes the authorized user unable to login to the account by changing the login credentials. Security in OSN must be provided and it must prevent the attacks aimed by the attackers. However, to prevent unauthorized access in OSN a novel authentication procedure is needed and the novel authentication mechanism must abide the social network platform’s characteristics. This paper considers the attackers who aim to access the OSN account in an unauthorized manner and presents two novel authentication mechanisms to prevent the OSN platform. The proposed procedures make use of users’ chatting module statistics and relationship circle statistics to authenticate the users in Online Social Networks. This can be done by Multi kernel Fuzzy C-Means (MKFCM) Algorithm and Group Search Optimizer (GSO). These novel authentication procedures abide the OSN characteristics, as it makes use of Chatting and Relationship module.     

Information diffusion in distributed OSN: The impact of trusted relationships

Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to “active social contacts” leads to a graph with high network connectivity, where the nodes are still well-connected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.     

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.     

Factors affecting privacy disclosure on social network sites: an integrated model

The self-disclosure of personal information by users on social network sites (SNSs) play a vital role in the self-sustainability of online social networking service provider platforms. However, people’s levels of privacy concern increases as a direct result of unauthorized procurement and exploitation of personal information from the use of social networks which in turn discourages users from disclosing their information or encourages users to submit fake information online. After a review of the Theory of Planned Behavior (TPB) and the privacy calculus model, an integrated model is proposed to explain privacy disclosure behaviors on social network sites. Thus, the aim of this paper is to find the key factors affecting users’ self-disclosure of personal information. Using privacy calculus, the perceived benefit was combined into the Theory of Planned Behavior, and after some modifications, an integrated model was prescribed specifically for the context of social network sites. The constructs of information sensitivity and perceived benefit were redefined after reviewing the literature. Through a study on the constructs of privacy concern and self-disclosure, this article aims at reducing the levels of privacy concern, while sustaining online transactions and further stimulating the development of social network sites.     

‘To play or not to play’: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology

The present research extends prior work on the relationship between users and technology by examining users’ intention to explore a technology. Drawing on exploration and individual motivation theories, we developed and tested a model examining the effects of hedonic (i.e., personal innovativeness and cognitive absorption) and instrumental (i.e., performance expectancy and image enhancement) factors on individuals’ intentions to explore a technology over time. Based on a study of 94 users exposed to a new technology, with measurements taken at two points in time, we found that both instrumental and hedonic factors affect individuals’ intentions to explore, but their effects change over time such that as time goes by, the effect of personal innovativeness decreases and performance expectancy increases. In addition to our contributions and implications for research on technology acceptance, we present practical implications both for developers and managers, with a view toward helping the development and deployment of technologies that satisfy the evolution of users’ needs over time.     

Privacy concern and online personalization: The moderating effects of information control and compensation

Firms have at their disposal an increasing amount of personal information about consumers gathered through various means. Studies find that personalizing online interactions improves customer relationships and increases desirable behaviors, such as positive word-of-mouth and increased purchase intent. However, other research suggests that the use of personal information stimulates privacy concern, which has a negative effect on behavior. This study examines potential moderators of the negative effects of privacy concern on behavioral intentions in the context of personalized online interactions. Results show that increasing perceived information control reduces the negative effect of privacy concern on intentions to engage in positive behaviors. In contrast, the offer of compensation has no effect on the relationship between privacy concern and these behavioral intentions. However, compensation increases the salience of trust to privacy concern.     

Assessment of subjective influence and trust with an online social network game

The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.