Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

The Effect of Network Characteristics on Online Identity Management Practices

As online social networking permeates all aspects of personal and professional lives, users of social networking sites (SNSs) are more motivated than ever to manage their online identities to project a favorable impression of themselves to online audiences. This research builds on the boundary management perspective to gain a better understanding of online identity management practices by examining the relationship between characteristics of the online social network, including cognitive homogeneity and social tie variety and the use of identity management practices such as segmentation and self-enhancement. The proposed research model is tested using survey data. The findings suggest cognitive homogeneity is positively related to the use of both identity management practices, segmentation and self-enhancement, whereas social tie variety is positively related to segmentation, but not self-enhancement practices. We conclude with implications of the study results for research and practice, as well as a discussion of directions for future research.     

A study of social participation and knowledge sharing in the teachers' online professional community of practice

To facilitate professional development of teachers in the online context, the online community of practice (CoPs) has become an important platform in which individuals with similar interests or common goals get together to share their resources, develop working strategies, solve problems, and improve individual as well as organizational performance. In this study, we have collected self-reported knowledge-sharing behaviors from 321 members of the largest online professional CoP of teachers in Taiwan. The results show that closer connections among online CoP members can lead to greater recognition of and altruism towards others. Moreover, performance expectation and self-efficacy belief play essential roles in knowledge-sharing participation. Thus, the development of social relationships among online teacher members helps them obtain potential resources and reliable support through their social network. Also, teachers' membership in the online professional CoP fosters a prosocial attitude that heightens their willingness to share useful resources and solve other members' problems, both emotionally and instrumentally. Consequently, knowledge-sharing behaviors, in terms of knowledge giving and knowing receiving, are significantly predicted by prosocial commitment and performance expectation respectively. The implications to both research and practice are provided in this paper.     

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.     

社交网络中用户隐私推理与保护研究综述

如今微博和Twitter等社交网络平台被广泛地用于交流、创建在线社区并进行社交活动。用户所发布的内容可以被推理出大量隐私信息,这导致社交网络中针对用户的隐私推理技术的兴起。利用用户的文本内容及在线行为等知识可以对用户进行推理攻击,社交关系推理和属性推理是对社交网络用户隐私的两种基本攻击。针对推理攻击保护机制和方法的研究也在日益增加,对隐私推理和保护技术相关的研究和文献进行了分类并总结,最后进行了探讨和展望。     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network

The popularity of online social networks has created massive social communication among their users and this leads to a huge amount of user-generated communication data. In recent years, Cyberbullying has grown into a major problem with the growth of online communication and social media. Cyberbullying has been recognized recently as a serious national health issue among online social network users and developing an efficient detection model holds tremendous practical significance. In this paper, we have proposed set of unique features derived from Twitter; network, activity, user, and tweet content, based on these feature, we developed a supervised machine learning solution for detecting cyberbullying in the Twitter. An evaluation demonstrates that our developed detection model based on our proposed features, achieved results with an area under the receiver-operating characteristic curve of 0.943 and an f-measure of 0.936. These results indicate that the proposed model based on these features provides a feasible solution to detecting Cyberbullying in online communication environments. Finally, we compare result obtained using our proposed features with the result obtained from two baseline features. The comparison outcomes show the significance of the proposed features.     

Virtual vignettes: the acquisition,analysis, and presentation of social network data

Online social networks （OSNs） are immensely important part of the modern, developed society. However digital forensic investigators who have no experience online prevalent and have now become a ubiquitous and online social networks pose significant problems to Data will reside on multiples of servers in multiple countries, across multiple jurisdictions. Capturing it before it is overwritten or deleted is a known problem, mirrored in other cloud based services. In this article, a novel method has been developed for the extraction, analysis, visualization, and comparison of snapshotted user profile data from the online social network Twitter. The research follows a process of design, implementation, simulation, and experimentation. Source code of the tool that was developed to facilitate data extraction has been made available on the Internet.     

Attention to news and its dissemination on Twitter: A survey

In recent years, news media have been hugely disrupted by news promotion, commentary and sharing in online, social media (e.g., Twitter, Facebook, and Reddit). This disruption has been the subject of a significant literature that has largely used AI techniques – machine learning, text analytics and network models – to both (i) understand the factors underlying audience attention and news dissemination on social media (e.g., effects of popularity, type of day) and (ii) provide new tools/guidelines for journalists to better disseminate their news via these social media. This paper provides an integrative review of the literature on the professional reporting of news on Twitter; focusing on how journalists and news outlets use Twitter as a platform to disseminate news, and on the factors that impact readers’ attention and engagement with that news on Twitter. Using the precise definition of a news-tweet (i.e., divided into user, content and context features), the survey structures the literature to reveal the main findings on features affecting audience attention to news and its dissemination on Twitter. From this analysis, it then considers the most effective guidelines for digital journalists to better disseminate news in the future.     

Celebrity's self-disclosure on Twitter and parasocial relationships: A mediating role of social presence

This study investigated how celebrities' self-disclosure on personal social media accounts, particularly Twitter, affects fans' perceptions. An online survey was utilized among a sample of 429 celebrity followers on Twitter. Results demonstrated that celebrities' professional self-disclosure (e.g., sharing their work-related life), personal self-disclosure (e.g., sharing their personal life such as friends and family), and fans' retweeting behavior, enhanced fans’ feeling of social presence, thereby positively affecting parasocial interaction with celebrities. Further, the study found that the effects of self-disclosure and retweeting on parasocial interaction were mediated by social presence. Implications and future research directions are provided.     

How Social Network Site Users’ Motives Predict Their Online Network Sizes: A Quantile Regression Approach to Japanese Twitter Usage

This study examines the relationships between Twitter users’ motives for using the service and their egocentric network sizes on Twitter in terms of online social capital. Based on the literature, we focus on quantiles of egocentric network sizes rather than on means. The respondents were 1,559 Japanese Twitter users; they participated in an online survey and allowed us to collect their log data on Twitter. A socializing motive was associated with the number of mutual follows only in the lower tails of the size distribution and was negatively linked to the number of one-sided follows. In contrast, an information-seeking motive was positively related to the number of one-sided follows. These findings suggest that cognitive constraints exert an effect on socializing through an online service.     

Temporal enactment of resettled refugees' ICT‐mediated information practices

“In this paper, we explain how resettled refugees use information and communication technology (ICT) to respond to their changed circumstances and, in doing so, enhance their well‐being and effective participation in a new society. Focusing on three modes of ICT‐mediated information practices (ie, orienting, instrumental, and expressive), we identify eight patterns of ICT use: learning about a new environment, keeping informed, transacting online, communicating with others, managing everyday life, sustaining support networks, maintaining transnational ties, and expressing cultural identity. Further, we draw on a temporal theory of human agency to explain how current dilemmas and contingencies, cultural identities and connections to the past, and future expectations and aspirations shape resettled refugees' enactment of these patterns of ICT‐mediated information practices. We show that, as resettled refugees move between multiple and overlapping temporal‐relational contexts, ICT use makes a difference to managing their bifurcated lives.”     

Understanding the online information‐seeking behaviours of young people: the role of networks of support

Information seeking is one of the most popular online activities for young people and can provide an additional information channel, which may enhance learning. In this study, we propose and test a model that adds to the existing literature by examining the ways in which parents, schools, and friends (what we call networks of support) effect young people's online information behaviours, while at the same time taking into account young people's individual characteristics, confidence, and skills to use the Internet. Using path analysis, we demonstrate the significance of networks of support in understanding the uptake of online information seeking both directly and indirectly (through enhancing self‐concept for learning and online skills). Young people who have better networks of support, particularly friends who are engaged in technology, are more likely to engage in online information seeking. While quantitative models of this nature cannot capture the complexity of individual online search practices, these findings may assist in the development of policy and practice to support young people to make the most effective use of the Internet for information seeking.     

Who is Who on Twitter–Spammer,Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.     

Enriching in-person encounters through social media: A study on family connectedness for the elderly

Social media sites have become immensely popular. In 2010 it was estimated that Americans spent a quarter of their online time using social networking sites (SNSs) and blogs. Prior studies have shown how people spend more time socializing through digital communication services such as SNSs reducing face-to-face interaction. Individuals limited offline interactions cause a sense of self-perception of being less socially involved. In this paper we explore how the use of an ubiquitous system we developed, Tlatoque, is able to adapt and move the SNS's social capital outside the desktop into a domestic setting to support older adults' offline interactions with their family. The findings of a 21 week deployment study uncovered the offline practices surrounding the use of Tlatoque and its social implications toward the existing family ties (n=30). Results qualitatively indicate that the content shared in SNSs strengthens older adults' social network by enriching and complementing traditional social engagements such as those conducted over the phone or in-person.     

Long Chains or Stable Communities? The Role of Emotional Stability in Twitter Conversations

In this article, we address the issue of how emotional stability affects social relationships in Twitter. In particular, we focus our study on users’ communicative interactions, identified by the symbol “@.” We collected a corpus of about 200,000 Twitter posts, and we annotated it with our personality recognition system. This system exploits linguistic features, such as punctuation and emoticons, and statistical features, such as follower count and retweeted posts. We tested the system on a data set annotated with personality models produced by human subjects and against a software for the analysis of Twitter data. Social network analysis shows that, whereas secure users have more mutual connections, neurotic users post more than secure ones and have the tendency to build longer chains of interacting users. Clustering coefficient analysis reveals that, whereas secure users tend to build stronger networks, neurotic users have difficulty in belonging to a stable community; hence, they seek for new contacts in online social networks.     

It is about timing: Network prestige in asynchronous online discussions

Asynchronous online discussions are broadly used to support social learning. This paper reports on an undergraduate class's online discussion activities over one semester. Applying social network analysis, this study revealed a participation gap among students reflected by their varied levels of network prestige. The low‐prestige group initiated equivalent volumes of interactions but were less reciprocated. In‐depth analysis found the high‐prestige group also advantageous in other network measures such as closeness centrality and eigenvector centrality, as well as the strength, persistence, and reciprocity of their ties. To probe potential explanations of the revealed gap, we further contrasted post content and posting behaviours between two groups. Results did not identify any significant differences in post content but found low‐prestige students' participation less timely and more temporally compressed. This paper calls for attention to the participation gap in online discussions, microlevel temporal patterns of student activities, and practical means to scaffold student participation in asynchronous online discussions.     

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.     

移动社交网络中矩阵混淆加密交友隐私保护策略

随着移动设备和在线社交网络的快速发展,通过用户的个人属性配置文件匹配,能够帮助用户在邻近的社交网络中迅速找到和自己共同特征的朋友.然而,交友匹配很有可能泄漏用户的敏感信息,因此用户隐私得不到保障.提出一种移动社交网络中交友匹配过程中的隐私保护协议,用户利用混淆矩阵变换算法和内积计算实现交友过程中的隐私安全和高效的匹配;用户可以细粒度定义自己特征属性的特征权重,从而使匹配结果更精确.此外,利用机会分析模型模拟真实交友场景来保证交友的有效性.安全性分析表明,提出的方法更具有隐私性、可用性和更低的通信和计算开销.通过结合真实的社会网络数据进行测试和评估,对比结果显示,比现有解决方案更有效.     

Use of Facebook,Tuenti, Twitter and Myspace among young Spanish people

This research describes and analyses the use made by young Spanish people of Tuenti, Facebook, Twitter and Myspace, exploring several variables: level of functional knowledge; frequency of use; place of use; reason for use; purpose and main activity; recipients of communication; degree of difficulty, satisfaction and preference; and intentions regarding future use. We designed and administered an online questionnaire to 757 students enrolled in secondary education (7th–11th levels and Vocational Education and Training) at seven educational centres. The results show that young Spanish people know about and use social networks on a daily basis. Tuenti was the one used with most frequency, followed by Facebook and Twitter. Myspace was the least known and used. Female subjects had a greater functional knowledge of these networks and reported a higher regular use of them. Participants used the social networks in their homes and preferred Tuenti because it is easy to use, allows them to communicate with friends and classmates and provides them with acceptable satisfaction as regards their need for prestige, acceptance and approval through the creation and maintenance of groups of friends and the publication of their achievements and self-realisations. The implications of the results obtained for psychological and social development are discussed.