Using communities against deception in online social networks

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.

An increasing number of today's social interactions occurs using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, Flickr gathers amateurs and professionals of photography, etc. Albeit different, all these online platforms share an ingredient that pervades all their applications. There exists an underlying social network that allows their users to keep in touch with each other and helps to engage them in common activities or interactions leading to a better fulfillment of the service's purposes. This is the reason why these platforms share a good number of functionalities, e.g., personal communication channels, broadcasted status updates, easy one-step information sharing, news feeds exposing broadcasted content, etc. As a result, online social networks are an interesting field to study an online social behavior that seems to be generic among the different online services. Since at the bottom of these services lays a network of declared relations and the basic interactions in these platforms tend to be pairwise, a natural methodology for studying these systems is provided by network science. In this chapter we describe some of the results of research studies on the structure, dynamics and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies and computer science.

The effect of consumer participation in online social networking activities on their susceptibility to influence is investigated in a context of restaurant consumption. This research identified a positive relationship between consumers engagement in social networking sites (SNS) on their susceptibility to global consumption influence, which is a multidimensional factor consisting of conformity to trend, social prestige, and quality perception. Furthermore, consumer engagement in SNS and susceptibility to global consumption influence positively affect social influence on SNS. That is, consumers with higher participation in SNS are more prone to global consumer convergence and peer influence on SNS. As implications for tourism and hospitality businesses, strategies to manage consumer-to-consumer communication on social media are suggested.

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 Online Social Networks (OSNs), interpersonal communication and information\nsharing are happening all the time, and it is real-time. When a user initiates\nan activity in OSNs, immediately, he/she will have a certain influence in\nhis/her friendship circle naturally, some users in the initiator's friendship\ncircle will be attracted to participate in this activity. Based on such a fact,\nwe design a k-hop Collaborate Game Model, which means that an activity\ninitiated by a user can only influence those users whose distance are within\nk-hop from this initiator in OSNs. Besides, we introduce the problem of Revenue\nMaximization under k-hop Collaborate Game (RMKCG), which identifies a limited\nnumber of initiators in order to obtain revenue as much as possible.\nCollaborate Game Model describes in detail how to quantify revenue and the\nlogic behind it. We do not know how many followers would be generated from an\nactivity in advance, thus, we need to adopt an adaptive strategy, where the\ndecision who is the next potential initiator depends on the results of past\ndecisions. Adaptive RMKCG problem can be considered as a new stochastic\noptimization problem, and we prove it is NP-hard, adaptive monotone, but not\nadaptive submodular. But in some special cases, it is adaptive submodular and\nan adaptive greedy strategy can obtain a $(1-1/e)$-approximation by adaptive\nsubmodularity theory. Due to the complexity of our model, it is hard to compute\nthe marginal gain for each candidate user, then, we propose a convenient and\nefficient computational method. The effectiveness and correctness of our\nalgorithms are validated by heavy simulation on real-world social networks\neventually.\n

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.

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.

Many business applications rely on their historical data to predict their business future.The marketing products process is one of the core processes for the business.Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time.Moreover, services are considered recently as products.The development of education and health services is depending on historical data.For the more, reducing online social media networks problems and crimes need a significant source of information.Data analysts need to use an efficient classification algorithm to predict the future of such businesses.However, dealing with a huge quantity of data requires great time to process.Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications.The classification technique is one of the most widely used with a variety of algorithms.In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications.A comprehensive analysis is made after delegated reading of 20 papers in the literature.This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education.Results show FFBPN is the most accurate algorithm in the business domain.The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities.Naïve Bayes algorithm is the most accurate to classify agriculture datasets.OneR is the most accurate algorithm to classify instances within the health domain.The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

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.

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.

Online Social Network’s (OSN especially Face book has experienced a growth in recent years. A social networking service is an online service, platform, or site that focuses on facilitating the building of social networks or social relations among people who, for example, share interests, activities, backgrounds, or real-life connections. A social network service consists of a representation of each user (often a profile), his/her social links, and a variety of additional services. Most social network services are webbased and provide means for users to interact over the Internet, such as e-mail and instant messaging. Online community services are sometimes considered as a social network service, though in a broader sense, social network service usually means an individual-centered service whereas online community services are group-centered. Social networking sites allow users to share ideas, activities, photos, videos, statuses, events, and interest within their individual networks. KeywordsCollaboration management, malicious activities, multiparty access control, social network.

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.

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.

Current online social networking (OSN) sites pose severe risks to their users' privacy. Facebook in particular is capturing more and more of a user's past activities, sometimes starting from the day of birth. Instead of transiently passing on information between friends, a user's data is stored persistently and therefore subject to the risk of undesired disclosure. Traditionally, a regular user of a social network has little awareness of her privacy needs in the Web or is not ready to invest a considerable effort in securing her online activities. Furthermore, the centralised nature of proprietary social networking platforms simply does not cater for end-to-end privacy protection mechanisms. In this paper, we present a non-disruptive and lightweight integration of a confidentiality mechanism into OSNs. Additionally, direct integration of visual security indicators into the OSN UI raise the awareness for (un)protected content and thus their own privacy. We present a fully-working prototype for Facebook and an initial usability study, showing that, on average, untrained users can be ready to use the service in three minutes.

Factors for success in online social networks: An fsQCA approach