All the news that’s fit to post: A profile of news use on social networking sites

Facebook and other social networking sites (SNSs)¹ are altering the way individuals communicate. These online environments allow users to keep up with friends, network with colleagues, and share their personal views and observations with others. Previous work describes typical social networking site users as young, extroverted, and technologically savvy. Little research exists, however, on the emerging role of news in the social network environment. With over 500 million global Facebook users, both print and television based media outlets are making concerted efforts to become part of this important and increasingly ubiquitous virtual world. The present study uses a sample of students, faculty, and staff from a large university to investigate the factors that are related to news use on Facebook. Findings indicate that while news use is still a minor component of overall social network site activity, certain key variables, such as gender and life satisfaction, have a significant impact on how Facebook is used for news-related purposes. Future implications for news in the social networking world are presented and discussed.     

A defence scheme against Identity Theft Attack based on multiple social networks

Recently, on-line social networking sites become more and more popular. People like to share their personal information such as their name, birthday and photos on these public sites. However, personal information could be misused by attackers. One kind of attacks called Identity Theft Attack is addressed in on-line social networking sites. After collecting the personal information of a victim, the attacker can create a fake identity to impersonate this victim and cheat the victim’s friends in order to destroy the trust relationships on the on-line social networking sites. In this paper, we propose a scheme to protect users from Identity Theft Attacks. In our work, users’ personal information can be still kept public. It means that this scheme does not violate the nature of the social networks. Compared with previous works, the proposed scheme incurs less overhead for users. Experimental results also demonstrate the practicality of the proposed scheme.     

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.     

社交网Twitter平台的人物关系网社区发现

社交网络服务（social networking service,SNS）已融入到大众生活中。人们将自己的信息上传到网络中,并通过社交网站管理自己的社交圈子,由此造成大量的个人信息在社交网络上被公开。文章基于Twitter平台,设计实现了Twitter用户关系网的社区发现。通过实时采集Twitter用户信息,重建人物关系网,改进Newman快速算法划分社区发现人物关系网。文章通过可视化的界面呈现用户的社区关系,提供用户网络行为,为决策者的舆情监控或个性推荐提供了参考凭据。     

Internet social network communities: Risk taking,trust, and privacy concerns

Individuals communicate and form relationships through Internet social networking websites such as Facebook and MySpace. We study risk taking, trust, and privacy concerns with regard to social networking websites among 205 college students using both reliable scales and behavior. Individuals with profiles on social networking websites have greater risk taking attitudes than those who do not; greater risk taking attitudes exist among men than women. Facebook has a greater sense of trust than MySpace. General privacy concerns and identity information disclosure concerns are of greater concern to women than men. Greater percentages of men than women display their phone numbers and home addresses on social networking websites. Social networking websites should inform potential users that risk taking and privacy concerns are potentially relevant and important concerns before individuals sign-up and create social networking websites.     

Understanding how big data leads to social networking vulnerability

Although the term “Big Data” is often used to refer to large datasets generated by science and engineering or business analytics efforts, increasingly it is used to refer to social networking websites and the enormous quantities of personal information, posts, and networking activities contained therein. The quantity and sensitive nature of this information constitutes both a fascinating means of inferring sociological parameters and a grave risk for security of privacy. The present study aimed to find evidence in the literature that malware has already adapted, to a significant degree, to this specific form of Big Data. Evidence of the potential for abuse of personal information was found: predictive models for personal traits of Facebook users are alarmingly effective with only a minimal depth of information, “Likes”, It is likely that more complex forms of information (e.g. posts, photos, connections, statuses) could lead to an unprecedented level of intrusiveness and familiarity with sensitive personal information. Support for the view that this potential for abuse of private information is being exploited was found in research describing the rapid adaptation of malware to social networking sites, for the purposes of social engineering and involuntary surrendering of personal information.     

The Move to Make Social Data Portable

Social networks such as Bebo, Flickr, Facebook, Friendster, Linkedln, MySpace, and Orkut have attracted millions of users who are involved in blogging, participatory book reviewing, personal networking, photo sharing, and other similar activities. The users have freely submitted personal data to proprietary databases so that they can communicate and share with other participants. However, some have begun to grumble that the networks' proprietary code and lack of APIs don't allow them to export data from one site to another. For social-network operators, this information lock-in was supposed to be an advantage. Forcing participants to re-enter data for each service ostensibly would encourage them to stay with the same service." Data portability allow us to take data, increasing competition [among sites] and creating a better environment for everyone using the Web.     

Making recommendations by integrating information from multiple social networks

It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted experiments have shown that using multiple features from multiple sources is expected to produce a more concrete and wider perspective of user’s behavior and preferences. This leads to improved recommendation outcome.     

The impact of engagement with social networking sites (SNSs) on cognitive skills

The aim of the present study was to investigate the effect of social networking sites (SNSs) engagement on cognitive and social skills. We investigated the use of Facebook, Twitter, and YouTube in a group of young adults and tested their working memory, attentional skills, and reported levels of social connectedness. Results showed that certain activities in Facebook (such as checking friends’ status updates) and YouTube (telling a friend to watch a video) predicted working memory test performance. The findings also indicated that Active and Passive SNS users had qualitatively different profiles of attentional control. The Active SNS users were more accurate and had fewer misses of the target stimuli in the first block of trials. They also did not discriminate their attentional resources exclusively to the target stimuli and were less likely to ignore distractor stimuli. Their engagement with SNS appeared to be exploratory and they assigned similar weight to incoming streams of information. With respect to social connectedness, participants’ self-reports were significantly related to Facebook use, but not Twitter or YouTube use, possibly as the result of greater opportunity to share personal content in the former SNS.     

Control your Facebook: An analysis of online privacy literacy

For an effective and responsible communication on social network sites (SNSs) users must decide between withholding and disclosing personal information. For this so-called privacy regulation, users need to have the respective skills—in other words, they need to have online privacy literacy. In this study, we discuss factors that potentially contribute to and result from online privacy literacy. In an online questionnaire with 630 Facebook users, we found that people who spend more time on Facebook and who have changed their privacy settings more frequently reported to have more online privacy literacy. People with more online privacy literacy, in turn, felt more secure on Facebook and implemented more social privacy settings. A mediation analysis showed that time spend on Facebook and experience with privacy regulation did not per se increase safety and privacy behavior directly, stressing the importance of online privacy literacy as a mediator to a safe and privacy-enhancing online behavior. We conclude that Internet experience leads to more online privacy literacy, which fosters a more cautious privacy behavior on SNSs.     

Psychological Determinants of Using Facebook: A Research Review

In recent years, Facebook has become the most popular of social networking sites (SNSs). Due to its increasing popularity and rising number of its users, the phenomenon of Facebook has aroused academic interest as well. There has been a growing number of studies on this subject. The aim of this article is to present the main trends in Facebook research and to provide an overview of major empirical findings. Among the most intensively explored topics in Facebook research, studies that concentrate on personality and individual differences among users, the role of self-efficacy, and motivation for using that specific SNS were identified. There is also a growing trend in empirical studies that focuses on testing advanced theoretical models of Facebook usage determinants. Technology acceptance model, presented in this article, is one of the most often used among them. This kind of approach may serve as a suggestion for a methodological conceptualization in the future confirmatory research on Facebook.     

社交网络用户浏览轨迹信息隐私保护方法仿真

随着网络应用的广泛普及,QQ、微信、YY语音、陌陌等社交软件走进千家万户,但社交网络用户浏览轨迹信息隐私保护问题也随之而来。由于社交网络平台安全机制存在漏洞,抵御网络攻击性能不强,使社交网络用户信息纷纷泄露。针对问题根源,提出ACP用户隐私信息防护系统,建立社交网络用户真空登陆模块(VM)、通讯信息密码文模块(RDT)及信息储存保护墙模块(LDM)一体化ACP用户隐私信息防护系统,从根源保护社交网络用户浏览轨迹信息的隐私安全。通过数据模拟仿真实验证明提出的ACP用户隐私信息防护系统,对社交网络用户浏览轨迹信息隐私保护具有可用性与有效性。     

Empirical investigation on factors influencing the behavioral intention to use Facebook

There continues to be a gap in understanding of what makes an individual engage in Facebook use. This is the primary research question of this study. Social network research is a good place to begin. Hence, this research aims to investigate factors influencing the behavioral intention to use Facebook. Cross-sectional data were collected from 200 respondents through a survey. Data were then analyzed by means of multiple regression analysis. The findings verified the research hypotheses and confirmed that perceived enjoyment, perceived ease of use, and perceived usefulness all impact attitude toward continuance intention of Facebook. This paper provides some insights into how practitioners could better develop Facebook site and enhances understanding of users?? perception toward Facebook. Facebook developer should evaluate ways to improve perceptions of their sites on these dimensions. Facebook group should add more game zones and interesting features to boost up the number of Facebook users so that Facebook can maintain or achieve a higher position in social networking sites compared to others. Implications for research and practice are discussed.     

A picture is worth a thousand words: A content analysis of Facebook profile photographs

This research examines identity construction and gender roles in social networking sites by studying and comparing the profile photographs of male and female Facebook users. Specifically, the number of photos in the profile album and the content of the main profile picture are studied by coding specific pictures, and determining if the content and amount of profile pictures differ significantly by gender. Participants include male and female Facebook users between the ages of 18 and 23 who are currently enrolled in a college or university. Profile pictures tended to be inactive, posed, appropriate, and only including the subject. The content and amount of Facebook profile photographs also did not significantly vary by gender. Implications of these findings, as well as suggestions for future research, are discussed.     

Using Facebook out of habit

This article investigates the uses and gratifications of the popular social networking site Facebook. In the exploratory stage, 70 users generated phrases to describe the manner they used Facebook. Interestingly, some users not only described the uses, but also mentioned how they perceive these uses. These phrases were coded into 14 items and clustered into four factors. The principal component analysis that was conducted in the third stage of the study, which was addressed to 222 Facebook users, verified the validity of the four factors: Social Connection, Social Network Surfing, Wasting Time and Using Applications. Previous user studies on Facebook have examined the immediate social effects of this popular social networking site, but they have not regarded emerging uses of the platform, such as gaming and applications, which do have a social component as a feature and not as a core principle. The ‘Wasting Time’ factor and the growth of ‘Using Applications’ factor indicate that Facebook has already become an integral part of daily computing routine, alongside with the rest of the entertainment desktop and web applications.     

An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data

Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.     

Differences Between People Who Use Only Facebook and Those Who Use Facebook Plus Twitter

Facebook (FB) and Twitter are popular social networking sites. This study examined differences between those who use both sites versus only FB, to test the hypothesis that mono-users differ in their personality characteristics from users active in both websites. Participants were 205 undergraduate students: 96 only used FB, 109 used FB and Twitter. Participants who used both sites reported significantly lower loneliness, higher number of FB friends, and lower number of minutes spent online, as compared to those who used only FB. Loneliness was positively associated with FB use only in those who used FB alone but was negatively associated with and negatively predicted both FB and Twitter use in those who used both websites. Findings suggest that more intense use of online interactions is more frequently found in mono-users (people using only Facebook) as compared to those using both websites, and it is predicted by increased feelings of loneliness. The current study findings provide additional insights on what personality factors may make some people prone to excessive use of social networking sites.     

A privacy self-assessment framework for online social networks

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection “weapons” are the users themselves. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. Moreover, even when social networking platforms allow their participants to control the privacy level of every published item, adopting a correct privacy policy is often an annoying and frustrating task and many users prefer to adopt simple but extreme strategies such as “visible-to-all” (exposing themselves to the highest risk), or “hidden-to-all” (wasting the positive social and economic potential of social networking websites). In this paper we propose a theoretical framework to i) measure the privacy risk of the users and alert them whenever their privacy is compromised and ii) help the users customize semi-automatically their privacy settings by limiting the number of manual operations. By investigating the relationship between the privacy measure and privacy preferences of real Facebook users, we show the effectiveness of our framework.     

Discovering target groups in social networking sites: An effective method for maximizing joint influential power

With the tremendous popularity of social networking sites in this era of Web 2.0, increasingly more users are contributing their comments and opinions about products, people, organizations, and many other entities. These online comments often have direct influence on consumers’ buying decisions and the public’s impressions of enterprises. As a result, enterprises have begun to explore the feasibility of using social networking sites as platforms to conduct targeted marking and enterprise reputation management for e-commerce and e-business. As indicated from recent marketing research, the joint influential power of a small group of active users could have considerable impact on a large number of consumers’ buying decisions and the public’s perception of the capabilities of enterprises. This paper illustrates a novel method that can effectively discover the most influential users from social networking sites (SNS). In particular, the general method of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with max joint influential power are proposed. The empirical evaluation with real data extracted from social networking sites shows that the proposed method can effectively identify the most influential groups when compared to the benchmark methods. This study opens the door to effectively conducting targeted marketing and enterprise reputation management on social networking sites.