All our messages are belong to us

In the world of e-marketing, new business models are introduced to accommodate changes caused by various factors, including the markets, the services, the customers, among others. One latest trend of e-marketing is social networking Web sites, many of which have attracted not only large number of users and visitors, but also business companies to place their online ads on the sites. As an important example of Web 2.0 applications, online social networks deserve comprehensive studying and analysis; they are not only employed as an effective vehicle of e-marketing, but may impact how future Web-based applications would be developed. In this chapter, we explore online social networking as a new trend of e-marketing, by conducting a comparative analysis of online social networking sites. We first discuss the various types of online social networks, based on the classification by Laudon & Traver (2008), and then analyze online social networks from a business strategy point of view, by discussing the primary revenue models for online social networking sites. The primary contribution of this chapter is a comparative analysis and discussions of representative online social networking sites and their respective revenue model(s). This chapter aims to provide the reader with a basic understanding of the emerging online social networking Web sites and their primary revenue models.

In the last decade, online social networking websites have become immensely popular worldwide, with the number of users growing exponentially. With a social network account, users can post an array of personal information and link their profile to those of other users, forming virtual communities. In the last few years, security and protection of private information posted on social networking sites has piqued the interest of researchers. Generally, the focus of this research has been limited to describing the type of information posted by users, with few studies examining the relationship between users' adoption of security measures for their social networking sites and negative online experiences, such as online interpersonal victimization. Our examination of the relationship between users' online social network activity, online social network security, and online interpersonal victimization found that users who engaged in risky online behaviors, such as opening numerous social network accounts and adding strangers as friends, were more likely to be victimized online.

Relying on social integration theory, this paper develops a conceptual model to explain how college students' online social networking (i.e., using Facebook) influences their social and academic social integration, and learning outcomes. Several rounds of focus group discussions were conducted to explore college students' current online social networking experience and their attitude toward using Facebook for education. Our findings indicate that students' online social networking is straightforwardly influential to their social learning, while its influence on the academic learning might be realised through a gradual and longitudinal process.

Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.

BackgroundThe dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change.ObjectiveThe intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions.MethodsEight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthy and disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized.ResultsA total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity).ConclusionsTo date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.

Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision. Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.

The third-party enterprises, such as sociologists and commercial companies, are mining data published from online social network (OSN) websites (e.g., Face book, Twitter) to serve their diverse purposes. This process leads to critical user concerns over their privacy, especially sensitive relationship with others on OSNs. Existing anonymization techniques in publishing online social data are focused on user identities, as users' relationship privacy will be automatically protected in general, if their identities are hidden. However, in reality, some users can still be identified from an identity-anonymized OSN by an attacker, as an individual user may publish his personal information to the public, through blog for example, which can be exploited by the attacker to re-identify the user from the published data. Therefore, we intend to preserve relationship privacy between two users one of whom can even be identified in the released OSN data. We define the ℓ-diversity anonymization model to preserve users' relationship privacy. Additionally, we devise two algorithms to achieve the ℓ-diversity anonymization - one only removes edges while the other only inserts vertices/edges for maintaining as many topological properties of the original social networks as possible, thus retaining the utility of the published data for the third-parties. Extensive experiments are conducted on both synthetic and real-world social network data sets to demonstrate that except from the achievement of privacy preservation, the utility loss caused by our proposed graph manipulation based techniques is acceptable. Besides, we analyze the influence of social network topology (e.g., average degree, network scalability) on the performance of our algorithms.

With the increasing number of online social networking site users, there is increasing cyber attacks on online social networks. Strong connectivity among users of the social networking site makes the information of interest rapidly spread. If the worm disguised as information to attract users' interest spreads on online social networking sites, it can cause great damage. Therefore, a fast patch propagation schemes need to be able to inhibit the activity of the worm. In this paper, we propose a fast patch propagation scheme based on genetic algorithms for online social networks.

The privacy control models of current Online Social Networks (OSNs) are biased towards the content owners' policy settings. Additionally, those privacy policy settings are too coarse-grained to allow users to control access to individual portions of information that is related to them. Especially, in a shared photo in OSNs, there can exist multiple Personally Identifiable Information (PII) items belonging to a user appearing in the photo, which can compromise the privacy of the user if viewed by others. However, current OSNs do not provide users any means to control access to their individual PII items. As a result, there exists a gap between the level of control that current OSNs can provide to their users and the privacy expectations of the users. In this paper, we propose an approach to facilitate collaborative control of individual PII items for photo sharing over OSNs, where we shift our focus from entire photo level control to the control of individual PII items within shared photos. We formulate a PII-based multiparty access control model to fulfill the need for collaborative access control of PII items, along with a policy specification scheme and a policy enforcement mechanism. We also discuss a proof-of-concept prototype of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology.

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users' private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs.

The 21st century has emerged as an era of accountability and standardisation for the teaching profession (Mikulec & Miller, 2012). The current research is comprised of 4 interrelated empirical studies which measure and explore individual differences in perceptions of teacher-student boundaries in the secondary school context from an ethical perspective. This research is timely given it has been 8 years since the Teacher Code of Conduct was introduced in Victoria (The Institute, 2008). While the relationship between teachers and their students has been recognised as a powerful contributor to learning, engagement, and development, the professional limitations of teacher-student interactions have been relatively unexplored. Further, technological advances in the last decade have created new environments that potentially expose teachers to new possibilities and vulnerabilities such as Social Networking Sites (SNS). Within a contingency framework for ethical decision-making (Ferrell & Gresham, 1985) the current research employs a mixed-methods approach and a cross-sectional design. Participants were preservice secondary teachers (N = 288) enrolled at a Victorian university and early career secondary teachers (N = 227) registered in Victoria. All participants completed a questionnaire during class time (preservice) or online (early career) which took approximately 40 minutes. In the absence of a measure of attitudes towards teacher-student interactions and behaviours, the Professional Interactions and Behaviours Scale (PIBS) was developed and validated in Study A. Based upon the Victorian Teacher Code of Conduct, (The Institute, 2008), 5 types of teacher-student interactions were educed from the items; emotional support, physical support, teacher disclosure, connectivity, and befriending. Although average endorsement was modest, early career teachers reported higher endorsement of teacher disclosure and preservice teachers provided greater approval of connectivity and befriending. An opportune international collaboration enabled further comparisons with preservice secondary teachers in Norway (N = 227), who demonstrated greater endorsement of the provision of physical support to students than Victorian preservice teachers. Differences in reported ethical training provided during teacher education are also noted and considered as a context for the development of attitudes toward professional behaviour. Utilising a person-centred approach, Study B explored profiles of trait-like characteristics among the Victorian preservice teachers through cluster analysis. This study proposed that individual differences in personality (Mc Crae & Costa, 2003) and attachment style as defined by Bartholomew (1990) may relate to other personal characteristics such as the need for popularity, domains of contingent self-worth and perceptions of professional behaviour as measured by the PIBS. Preservice teachers described as “responsive resilient” in personality were more likely than “curious & sensitive ambiverts” to endorse teacher provision of emotional support to students. Further, securely attached individuals were more likely to endorse the provision of physical support than dismissive individuals. Study C utilised a mixed-methods approach to explore profiles of teacher-student interactions styles in early career secondary teachers and their responses to ethical dilemmas relating to teacher-student boundaries. Four profiles emerged; “sparing investors”, “reserved professionals”, “relational enthusiasts”, and “supportive ambitious” and in terms of interpersonal goals, professional expectancies and values, burnout, career satisfaction and career aspirations. Few quantitative differences were observed between profiles in terms of ethical decision-making, yet thematic analysis highlighted contextual considerations and protective behaviours as key considerations of early career teachers. Study D explored current and prospective social networking behaviour and found both preservice and early career teachers were highly engaged with SNS, with preservice teachers reporting greater usage. Almost half of participants in both the preservice and early career samples reported negative experiences as a result of online social networking such as bullying or workplace issues. In relation to connecting with students, early career teachers were more likely to accept a friend request from a past student than preservice teachers. Taken together, these studies explore individual differences in teacher-student boundaries as they are perceived by preservice and early career teachers in Victoria. The PIBS is presented as a valid contemporary measure of perceptions towards teacher-student interactions, and differences of experience and culture considered. Implications of the findings are considered in relation to teacher selection processes, the ethics curriculum in teacher education, and regulation of the teaching profession in Australia.

Using communities against deception in online social networks

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

Background Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. Purpose The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. Methods We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. Results The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.

Purpose of the study: This study analysed the relationship between Online Social Networking websites and Applications (OSNSA) and university students' academic achievement.
 Methodology: In the present research, a quantitative research design was followed, and a survey research type of descriptive research was used in this study. Data were collected from 150 university students using a modified version of The Online Social Networking Sites scale (Paul, Baker, & Cochran, 2012). The correlation was calculated by using the SPSS 22nd version.
 Main Findings: The findings revealed a connection between students' time spent on OSNSA and their academic outcomes. The time spent on OSNSA was observed to be strongly influenced by the attention span of the students. In particular, we find that a lower academic output is provided by the greater time spent on OSNSA. Also, attention span was strongly associated with features that influence students' academic success, such as their perceptions of society's social networking outlook, their advantages, and disadvantages of OSNSA, use of OSNSA, etc.
 Applications of this study: Many students spend most of their hours on online networking pages, like Facebook, Tok-Tok, and WhatsApp, which are the most common, considering the harmful impact of social media. In certain eyes, social media has embedded a detrimental effect, but it is also used to acquire vital knowledge and social skills for pupils.
 Novelty/Originality of this study: This research is different from other studies because it explores the relationship between social networking sites & the application and academic performance of university students belonging from South Punjab by exploring various facets of OSNSA which ultimately promote university students’ academic performance.