Genetic algorithm based patching scheme for worm containment on social network

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.

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.

Professionalism conduct on social networks before and after COVID-19 for healthcare practitioners

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.

This chapter explores the unique forms of collaboration and civic engagement that are enabled by online social network sites (SNSs) within informal and formal learning settings. We highlight emerging research in the learning sciences and digital media and learning fields that illuminate the new possibilities for collaboration and civic engagement offered by this emerging social media. Here we focus especially, but not exclusively, on online SNSs and social networking applications. We offer two examples of SNSs that support collaboration and civic engagement in such spaces and beyond them and discuss their implications for educators and researchers. Finally, we conclude with a set of questions that will drive the research in the next decade.

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.

Using communities against deception in online social networks

This study looked at the relationship between local and online forms of community engagement for a small group of older people in the South of England. Interviews and a focus group were used to establish understandings of online community and social networking sites (SNS) within the context of existing relationships in local communities. Historical understandings of community influenced expectations for online community with four catalysts being identified which motivated engagement with community resources both online and locally. These were: ‘spaces and places', family, roles and loss. There was a clear preference for meeting people face to face with online communities and SNS being used predominantly as tools for achieving this aim. Exploration of the catalysts offers ways that greater community involvement might be further facilitated through design initiatives. Suggestions include private ‘family rooms', anonymous ‘sharing spaces' and a focus on hyperlocal initiatives to connect local and online communities.

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.

Social networking sites (SNS) have become an integral part of modern society. Among online social networks, Facebook is considered as one of the most popular platforms for online social networking among millions of users including students of all ages. This paper attempts to evaluate whether Japanese students consider Social networking site Facebook as effective online language-learning platform and whether this social learning approach provide students with the opportunity to voluntarily learn English as a second language and gain experiences that help them to better communicate with foreigners using English. A survey was carried out with 88 undergraduates, postgraduate and research students at University of Toyama, Japan to observe learners' perceptions and attitudes towards the usefulness of Facebook for online language learning. Data collected with the use of structured questionnaire from randomly selected students. Most of candidates agreed that Facebook offer great promises to facilitate them learn English and to improve their motivation as well as confidence level in peer- to- peer communication using English. The result of the survey demonstrated that students believed that Facebook could be used as an effective online platform for English learning and related online educational discussions. In conclusion, Facebook may serve as a meaningful and promising teaching tool which can be used not only by English language instructors but also by other educators for fostering quality in online education.

Online social networking sites have shown an unbelievable widening in the last decade. Spammers utilise social networking sites to unroll spam messages due to its fame and use various procedures to spread spam. Consequently, the identification of spam must be well fortified enough to detect unsolicited messages and deter spammers. Though various spam identification procedures are obtainable, to improve the accuracy for spam identification is inevitable. In this work, a method to detect unsolicited messages is proposed to recognise and avert spam messages. The social context parameters such as trust and strength as well as spam template matching are also considered along with basic classifiers for effective spam classification. The intercommunication factors between the users are used for strength calculation. Spam template generation is performed based on the majority merge operation on the spam messages during the training time, and spam templates comparison is performed with the incoming messages during the testing time. Trust value updation is performed after the message classification. Experimental results demonstrate that the proposed model with SVM-Polynomial Radial Basis kernel which provides better accuracy in spam classification and outperforms all the state-of-the-art methods.

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.

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.