Online Social Networks (OSNs) Research Papers

This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new da...

- by
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- Machine Learning, Mental Health, Deep Learning, Online Social Networks (OSNs)

As the ASEAN consumers are increasingly moving online, the number of internet users according to UBS increase to 32% across the region and 112% penetrated by mobile technology. More than 50% of ASEAN users are participating to online social network (OSN). This research is to investigate the awareness and influence of security and privacy issues on internets users’ trust, and building a safer OSN landscape in South East Asian region by examining the relationships among online privacy concerns, security, trust, and intention. Using structural equation modelling, the findings shows that ‘privacy’ correlates with ‘security’ but these two variables do not have significant impact on users’ trust. Moreover, only ‘trust’ and ‘security’ affect users’ intention to use OSN.

- by Cherry Narumon Sriratanaviriyakul and +2
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- Trust, Network Security, Structural Equation Modeling, Southeast Asia

The center issue in today's Online Social Networks (OSNs) is to dispense clients the specialist to deal with the messages posted on their private space to turn away that undesirable substance. The undesirable information may contains political, disgusting, non neural and so forth message sifting frameworks are intended for unstructured or semi-organized information, instead of database applications, which utilize extremely organized information. This venture proposed a System with the adaptable tenets to channel the undesirable messages posted on client divider. Subsequent to intersection edge esteem the warning message is send to that client. This permits clients to redo the refining criteria to be connected to their dividers, and a Machine Learning-based classifier consequently group the messages and naming messages in support of content-based sifting.

- by IJREAM EDITOR
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- Online Social Networks (OSNs)

This article examines the future of community music. It offers an interdisciplinary per spective by considering emerging research in CM, as well as related literature from edu cation, ethnomusicology, sociology, and media and communication studies. It examines is sues such as demographics, notions of communitas, international trends, and new net works for collaboration.

- by Waldron Janice and +1
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- Future Studies, Music, Community Music, Online Learning

The advent of sharing sensitive information via Online Social Networks (OSN) has jeopardized the user to the extent that the privacy of millions of OSN users could well be compromised, with their data openly available in the public domain. Evidently, users lack in data privacy and the access control mechanisms available to avoid the risk of disclosure. Therefore a framework that automatically preserves the user privacy to detect sensitive topic and minimize the risk of sensitive information disclosure risk beyond the current privacy sceneries offered by OSN service providers is required. In this paper, we present a threefold sanitization framework which precisely detects sensitive topics semantically using statistical topic model scheme which incorporates standard knowledge bases for tagging the sensitive topics discovered. The interaction documents from location-of-interest are subjected to SSAR-LDA using Gibbs Sampling to identify sensitive topic clusters with high location entropy. The experimental result shows, (i) the sensitive topic clusters are identified with very high accuracy, (ii) despite the redaction approach, which eliminate the sensitive term, our proposed scheme enhance the privacy preserving policy by replacing the sensitive terms with suitable hierarchical generalization fetched from knowledge bases (iii) the probability of Kullback-Leibler (KL) divergence between sensitive and generalized sanitization terms on Twitter, with negligible information disclosure risk is acceptable, and (iv) the sanitization carried out for 10 sensitive topics, from 4500 user posts of 790 Twitter users, demonstrated high precision and recall, which can be correlated with advanced privacy settings for OSN users in the near future.

At present social networks play an important role to express people's sentiment and interest in a particular field. Extracting a user's public social network data (what the user shares with friends and relatives and how the user reacts over others' thought) means extracting the user's behavior. Defining some determined hypothesis if we make machine understand human sentiment and interest, it is possible to recommend a user about his/her personal interest on basis of the user's sentiment analyzed by machine. Our main approach is to suggest a user regarding the user's specific interest that is anticipated by analyzing the user's public data. This can be extended to further business analysis to suggest products or services of different companies depending on the consumer's personal choice. This automation will also help to choose the correct candidate for any questionnaire. This system will also help anyone to know about himself or herself, how one's behavior may influence others. It is possible to identify different types of people such as- dependable people, leadership skilled, people of supportive mentality, people of negative mentality etc.

- by Shah Alam
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- Psychology, Data Mining, Big Five Personality Traits, Online Social Networks (OSNs)

The explosion of online social networks (OSN) has created an interactive and communicative global phenomenon that has enabled billions of users to connect to other individuals on Facebook and Twitter but also with media sharing platforms such as Instagram and Pinterest. This study examines the current use of social media platforms and explores the factors that help define the long term implications of social media. The study employed a nationwide survey collected from 2012 to 2013 and is available from the PEW Internet research center of more than 2000 American citizens' behaviour towards OSNs. The results revealed strong predictors of OSN that form the connections among users; and the core significant predictors: age, gender and access to mobile Internet that foster the adoption and usage of OSN in the future. Furthermore, online activities such as posting video content on social networks also highlighted the online usage patterns and trends of using social media to actively engage with other users more willingly than text. This is due to the viral nature of online media sharing on social media and as part of the video viewing and creating experience. An outline of practical implications of the findings and areas for future research is also discussed.

In this article, we explore the evolution of Online Social Networks (OSNs) by providing a brief history, evaluate the benefits and disadvantages and focus its impact in our society. We also share an historical perspective view of Facebook... more

- by Fábio Santos
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- Social Networks, Social Media, Online Social Networks (OSNs)
- by Khan M . R . Taufique
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- Adoption, Social Media, Social Media Marketing, Technology Adoption

Most of the research on the implications of mobile communication for social networks has focused on its uses and
consequences in the intimate realm of close friends, family, and loved ones. A number of scholars have also become
interested in ways that mobile communication helps and hinders the broader realm of network connectivity, including
diverse, weak, and new ties. A collection of theoretical perspectives on mobile communication and diverse, weak,
and new ties proposes that heightened connectivity in the intimate realm can come at the expense of being engaged
more broadly – a scenario I characterize as network privatism. At the same time, the available empirical research in
the literature tends to tell a different story, or rather stories. This analysis brings theory and empirical findings into
closer conversation with one another by reviewing and synthesizing the literature in this area. Observed patterns in
the literature offer new insight into questions of mobile communication and network privatism, while also pointing
to opportunities for refinement of theory, analysis, and measurement as this line of inquiry further develops.

- by Review of Communication Research - Open-Access Highest-Quality Literature Reviews and +1
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- Social Networks, Diversity, Cell Phones, Mobile Phones

Twitter's unique platform environment and readily accessible data have resulted in a copious number of studies aimed at understanding what, and who is influential across the Twittersphere, and why this influence arises. The interdisciplinary nature of these studies poses a potential barrier for future investigations to identify relevant literature. The purpose of this review is to summarise findings pertaining to what we define as Twitter-Influence from the perspective of user behaviour: retweeting, popularity and replies/mentions.

- by Caitlin McCurrie and +1
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- Social Network Analysis (SNA), Twitter, Social Influence, Information Sharing

SMA-Matematika-OSP-2003-Solusi

Our society is becoming technology dependent and this is particular true for people in academia. Online Social Network (OSN) is a method to connect students with their teachers within the academic environment. This paper reviews the usage of OSNs, education in other domains, the association between OSNs and learning style, as well as its effects in higher education. The review emphasises that OSNs play a vital role in enhancing the effectiveness of teaching and learning process in academia. However, without proper training programs and awareness against OSNs consequences, negative outcomes such as security and privacy issues will occur in academic institutions’ IT systems. This affects students and instructors’ personal

- by Mohammed Eltahir and +2
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- Security, Learning Styles, Web Applications, Academic Performance

One crucial issue in the present Online Social Networks (OSNs) is to enable clients to control the messages that are posted individually private space to evade that undesirable substance is shown. Up to now, OSNs give little help to this necessity. To fill the hole, in this paper, we propose a framework that permit OSN clients to have an immediate control on the messages that are posted on their dividers. This is accomplished through an adaptable standard based framework, which enables clients to change the separating criteria that is connected to their dividers, and a Machine Learning-based soft classifier consequently naming messages in help of content based filtering.

The socialization of the web has undertaken a new dimension after the emergence of the Online Social Networks (OSN) concept. The fact that each Internet user becomes a potential content creator entails managing a big amount of data. This paper explores the most popular professional OSN: LinkedIn. A scraping technique was implemented to get around 5 Million public profiles. The application of natural language processing techniques (NLP) to classify the educational background and to cluster the professional background of the collected profiles led us to provide some insights about this OSN’s users and to evaluate the relationships between educational degrees and professional careers.

Current changes in technology and the role of the Internet open up new opportunities for companies both to communicate and to work. The fact that Social Media entered the business landscape, and in particular the recruitment landscape leads to a demand for knowledge about recruitment trends regarding social media. Contemporary social media and recruitment texts have been reviewed in order to identify what impact social media is having on the recruitment industry. It focuses on how companies and recruitment professionals are taking advantage of the social media landscape to recruit talented people. Relevant existing research has been reviewed and primary research conducted (both qualitative and quantitative). The authors have conducted 8 IDIs with recruiter from various industries. The latter consisted of 147 CAWIs with employees from various Polish organizations from several different industries. The article presents interesting results that can be especially useful for recruitment managers, specialists and company owners. It is recommended that in order to gain a fuller picture of the issues underlying the findings, quantitative research on a bigger scale should be undertaken.

- by Michał Chmielecki
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- Social Networks, Social Media, Online social networks, Social Media Marketing

SELEKSI OLIMPIADE MATEMATIKA INDONESIA 2003 TINGKAT PROVINSI

Although social networking sites (SNSs) present a great deal of opportunities
to support learning, the privacy risk is perceived by learners as a friction
point that affects their full use for learning. Privacy risks in SNSs can be divided
into risks that are posed by the SNS provider itself and risks that result from user’s
social interactions. Using an online survey questionnaire, this study explored the
students’ perception of the benefits in using social networking sites for learning
purposes and their perceived privacy risks. A sample of 214 students from Uganda
Christian University in Africa was studied. The results show that although 88 % of
participants indicated the usefulness of SNSs for learning, they are also aware of the
risks associated with these sites. Most of the participants are concerned with privacy
risks such as identity theft, cyber bullying, and impersonation that might influence
their online learning participation in SNSs.

- by Francis Otto and +3
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- Privacy, Social Media, Online Learning, Social Networking Sites (SNS)

Arguably one of the most important features of Twitter is the support for “retweets” or messages re-posted verbatim by a user that were originated by someone else. Despite the fact that retweets are routinely studied and reported, many important questions remain about user motivation for their use and their significance. In this paper we answer the question of what users indicate when they retweet. We do so in a comprehensive fashion, by employing a user survey, a study of user profiles, and a meta-review of over 100 research publications from three related major conferences. Our findings indicate that retweeting indicates not only interest in a message, but also trust in the message and the originator, and agreement with the message contents. However, the findings are significantly weaker for journalists, some of whom beg to differ declaring so in their own user profiles. On the other hand, the inclusion of hashtags strengthens the signal of agreement, especially when the hashtags are related to politics. While in the past there have been additional claims in the literature about possible reasons for retweeting, many of them are not supported, especially given the technical changes introduced recently by Twitter.

- by Panagiotis Metaxas and +1
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- Social Media, Twitter, Online Journalism, Online Social Networks (OSNs)

Photo sharing is a captivating feature which popularizes Online Gregarious Networks (OSNs). Lamentably, it may leak users' privacy if they are sanctioned to post, comment, and tag a photo liberatingly. In this paper, we endeavor to address this issue and study the scenario when a utilizer shares a photo containing individuals other than him/her (termed co-photo for short). To obviate possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be cognizant of the posting activity and participate in the decision making on the photo posting. For this purport, we require an efficient facial apperception (FR) system that can apperceive everyone in the photo. However, more authoritatively mandating privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism endeavors to utilize users' private photos to design a personalized FR system concretely trained to differentiate possible photo co-owners without leaking their privacy. We additionally develop a distributed consensus predicated method to reduce the computational intricacy and for fend the private training set. We show that our system is superior to other possible approaches in terms of apperception ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook's platform.

In Online Social Networks (OSNs) there is a need for better understanding of social trust in order to improve the analysis process and mining credibility of social media data. Given the open environment and fewer restrictions associated with OSNs, the medium allows legitimate users as well as spammers to publish their content. Hence, it is essential to measure users' credibility in various domains and accordingly define influential users in a particular domain(s). Most of the existing approaches of trustworthiness evaluation of users in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users' influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present an approach to analysing domain-based user's trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user's content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated by considering temporal factor. We show the accuracy of the proposed algorithm by providing a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs using big data Infrastructure.

- by Bilal Abu-Salih
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- Information Retrieval, Semantic Analysis, Big Data, Online Social Networks (OSNs)

This paper examines the decisive roles of strain and addiction in post-acceptance behavior of social networking sites' users. Therefore, we focus in a first step on the formation of discontinuous usage intentions by theorizing strain, addiction, and satisfaction as direct influencing factor that causes and/or inhibits them. In a second step, the influence of these variables on the intention-behavior relation is focused. We theorize that addiction as well as the ratio between satisfaction and strain moderate whether users transfer discontinuous usage intentions into non-usage behavior. To validate the subsequent research model we propose a longitudinal research setting and present initial results, whereupon satisfaction and strain -but not addiction -causes discontinuous usage intentions. We discuss our expected contributions by revealing that satisfaction, strain, and addiction influence whether a technology is used continuously; however, their influence on intentions and actual behaviors differs.

Nowaday's Online Social Networks (OSN) are Experiencing high rise in popularity due to increase in number of users in it. and it is necessary to know the users connections with other users present in these social structures where it can be done through forming communities based on their connections. In this we have formulated procedure of problem of finding the communities and high influencer or important user in aonline social network. The Communities of users are formed using Louvain algorithm in the social network. and by using Degree and Betweenness Centrality measures we have obtained the top influencer in all obtained communities.

The spatial design of a home has an affinity to intimacy that might have been overlooked by many practicing architects. In this paper, the subject matter of housing will be discussed with a shifted focus from domestic buildings to the space of home and intimacy that scatters in a city. In recent years, some sociologists (Illouz, 2013 & 1997; Turkle, 2013; Jamieson, 2012) have underlined a social translation of a new spatial home by analysing an intimacy in a city that has been enabled by ICT technologies; business models i.e. Airbnb and Couchsurfing also indicated a new form of dwelling by demonstrating how share-economy can assist this spatial home and, in particular, assist the distribution of it so as to assist a city dweller to find and make home without possessing a house. And yet, neither of these examples of home are designed by architects who might actually know the best about the subject matter - this space of home and intimacy. What if the critical spatial design that an architect can produce isn’t the domestic buildings but the visualisation of this space? What is this critical spatial design of the home to cope with the shifting social needs already seen? Through Jeremy Till’s way of looking at buildings and beyond - the lens of Spatial Agency (2011) - the paper will further speculate these examples of home by architectural-writing this spatial design - this architectural-writing, as Jane Rendell describes in chapter of Essay Collections of Critical Architecture (2007), similarly to art-writing, “the very form of the writing itself is taken to be integral to the way in which a critic positions him/herself… The personal and autobiographical enter the debate, not in order to assert an ego criticism, but as part of an on-going political exploration of subjectivity.”

- by Weiyin Ma
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- Community Psychology, Social Psychology, Multiculturalism, Digital Humanities
- by Signe Pikk
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- Cyberbullying, Dynamic Analysis, Online Privacy, Internet regulation

Online Social Network is the network of people connected with the help of internet in the form of cluster of people. In this people are basically related to each other with some relationship types such as Family or Friends or with people to permuting of products. Owners are the one who is having co-owned and stakeholders are the data are being shared. To solve his/her purpose the owner will misuse the data of Stakeholder's by posting it some sites where the stakeholder's account will not be there for these situation privacy policy should be there to restrict the owner in that situation privacy may be compromised since the document can be approached by different users in network. OSN helps users to give the authorized access for the data Sharing. When users decide to post a data, it will be based on the combined opinion of all involved users. Floyd Shortest path algorithm is used to calculate the distance between the users's which further helps in calculating the trust values of each node. We proposed an algorithm called security of trust which helps the stakeholders for misusing of the co-owned data. The trust values between owner's and stakeholders are used to opinions, and accordingly the trust values are updated according to users' privacy loss and it also provide additional for the security of Online Social Network to reduce the leakage of information to the other security trade of between data Sharing.

- by IRJCS: : International Research Journal of Computer Science and +3
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- Privacy, Trust, Equity and Trusts, Online social networks

The Macedonia naming dispute has been an important issue in Greek affairs. It constitutes both an irresolvable, decades-old international problem and a significant, yet undertheorised, analytical topic. In this context, our aim is to critically explore, highlight and discuss the deep-seated and pervasive patterns, representations, attitudes, beliefs, ideas and norms within the Greek social imaginary, as these emerged on Twitter in real-time, during the mass “Macedonia rally” on February 4, 2018. More specifically, drawing on the dialectical interaction between Twitter posts, sociopolitical behaviours and interpretative analytic frames linked to interdisciplinary theoretical discourses, we attempt to understand and interrogate the intellectual structures, value system and operational categories of a large number of Greek groups on the ‘Twittersphere’. Based on the assumption that, in the last instance, the rigid refusal of the majority of the Greek people to accept a ‘composite name’ solution is connected with the tacit social imaginary of the Greek society, the present paper brings to the fore a complex identity problem. This problem relationally refers to the internal workings of the individuals, the psyche and the unconscious, but also to hidden and unreflected symbolic backgrounds, macro-social processes, and cultural legacies. Our following Twitter network analysis, focused on selected hashtags regarding the ‘Macedonia rally’, point out the character of social dynamics and ascertain the findings of the interpretative research strand.

- by Charalambos Tsekeris and +1
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- Sociology, Political Sociology, Self and Identity, Social Networks

The ubiquity of mobile location aware devices and the proliferation of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influential LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence propagation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more restrictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method.

- by Nikos Bikakis and +1
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- Information Systems, Algorithms, Information Retrieval, Information Systems (Business Informatics)

This research aims to explain the negative side of ICT-enabled communications. Therefore, the perception of users that social interactions on online social networks (OSN) are threatening is suggested as a new variable called social interaction overload. The paper theorizes that individual, OSN-specific, and OSN-specific communication characteristics manifest the extent to which social interaction overload is perceived and how users response to it in a psychological and behavioral manner. Results of an empirical survey with 246 OSN users validate the assumed effects, so that we identify age, number of friends, and communication content as contribution factors of social interaction overload, which in turn has a direct effect on the two outcome variables satisfaction and continuous usage intention. Moreover, results reveal that social interaction overload has higher effects on OSN users' satisfaction than perceived usefulness or perceived enjoyment.

- by Christian Maier
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- Social Interaction, Stress, Facebook, Online social networks

E-learning has been practiced extensively across the globe, and thanks to Internet penetration and advancement it is progressively being adopted among students in the learning environment. E-learning systems are currently available from high schools to higher institutions of learning globally. However, the systems still lack in covering certain areas of the education sphere and conventional frameworks of e-learning are incapable of answering to those demands both for learners and tutors. This paper proposes a framework that integrates social networks into web-based learning environment to make the environments more interactive and personalized. This research paper shows increased social interactivity and personalization through user profiles in the e-learning environment. This paper also evaluates some of the existing frameworks of e-learning and introduces a new functional framework integrated with Facebook social network that aims to enhance personalized and interactive web-based learning environment. To evaluate the proposed framework's personalized interactivity, experiments and online surveys were conducted. An analysis of the data collected was carried out with the aim of trying to establish the existence of any relationship between the student's performances and increased social interactivity after the use of the proposed framework in the e-learning environment. Increased social interactivity among education actors, enhanced e-learning environment, better learning outcomes and performance and increased learner productivity and participation across cross-platforms were mentioned as some of the perceived impacts of social interaction in an e-learning environment. A total of 84% of the respondents reported lack of personalization and 66% mentioned inadequate e-learning infrastructure and unreliable information whilst 62% indicated inadequate training of lecturers or course instructors as significant barriers of e-learning systems not integrated with social network(s). The outcome of this research can be used to contribute to best teaching practices among lecturers and improve online learning experiences to students in institutions of higher learning.

- by Tito Wawire
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- Artificial Intelligence, Social Computing For Education, Smart Cities, Cyber Security

The use of new communications technologies and social media, in Greece, during the time of crisis, has led to the development of numerous online informal Civil Society Networks (CSNs) (i.e. networking-building platforms, self-organized groups in Facebook, forums, exchange platforms) proposing a rethinking of the status quo of formal civil organizations. This research, utilizing the methodology of discourse analysis, aims at summarizing the rise of these networks in Greece that incorporates both solidarity initiatives and autonomous political/economic spaces and identify the indicative predictive factors of their survival and growth. Some basic conclusions that have been drawn through this research is that alternative online networks can be proven as indicative sign of the social dynamism of a given period but in order to be resilient and sustainable they should develop focal points of physical reference, pursue national representation, focus mainly on mono-thematic goods/services and cultivate, in several cases, links with relevant social movements and local or national NGOs. A general induction through this research is that a CSN, during this current crisis, stands between two classical models of reference in a society seeking modernity and flexibility and can be considered as a proposed type of effective experimentation and mobilization that can pursue common social goals and serve needs of deprived people. Some issues that still remain underexplored and need further elaboration are social and political identity of participants, the potential links with local, national and international communities, the functional balance be

Online Social Network (OSN) has become the most popular platform on the Internet that can provide an interesting and creative ways to communicate, sharing and meets with peoples. As OSNs mature, issues regarding proper use of OSNs are also growing. In this research, the challenges of online social networks have been investigated. The current issues in some of the Social Network Sites are being studied and compared. Cyber criminals, malware attacks, physical threat, security and usability and some privacy issues have been recognized as the challenges of the current social networking sites. Trust oncerns have been raised and the trustworthiness of social networking sites has been questioned. Currently, the trust in social networks is using the single- faceted approach, which is not well personalized, and doesn’t account for the subjective views of trust, According to each user, but only the general trust believes of a group of population. The trust level towards a person cannot be calculated and trust is lack of personalization. From our initial survey, we had found that most people can share their information without any doubts on OSN but they normally do not trust all their friends equally and think there is a need of trust management. We had found mixed opinions in relation to the proposed rating feature in OSNs too. By adopting the idea of multi-faceted trust model, a user-centric model that can personalize the comments/photos in social network with user’s customized traits of trust is proposed. This model can probably solve many of the trust issues towards the social networking sites with personalized trust features, in order to keep the postings on social sites confidential and integrity.

Internet has a big influence in the life of the people in positive way. The usage of internet has increased enormously. In current years Online Social Networks also developed and plays an equivalent role. Online social networks are ideal for exchanging ideas, views and public opinion. Hence Online Social Networks should be highly secure and should protect the individual’s privacy. The Online Social Network provides the security measures but they were limited. During Socialization the user can access the profile of other members involved in social sites and even share data such as text, images etc. One crucial issue in user wall is to give users the ability to control the messages posted on their own confidential space in order to avoid unwanted content to be displayed. In this paper, we review filtering methods by using machine learning technique to filter the unwanted messages on the user walls.

- by IJCSMC Journal
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- Online social networks, Online Social Networks (OSNs)

In the context of the personalized information retrieval, this paper presents WebCap, a smart web browser for inferring the user’s interests based on a real time implicit feedback.
WebCap aims to collect implicitly the relevant documents, during
the user’s search session by inference of the degree of interest
based on a combination of some relevant implicit indicators. The
main advantage of our approach is that it does not require any log
file. Indeed, WebCap, a browser side tool, calculates the degree
of interest in real time and decides if the web page is quite
relevant to feed the search history dimension. Our experimental
framework, with real users, shows that WebCap can collected
implicitly 80% of the relevant documents compared to the explicit
approach.

The importance of analyzing social networks lies in enabling researchers to understand better the relationships among network actors over time through social data mining and patterns examining social network growth. This paper aims to analyze the topology structure and critical influences on Facebook for employees' group of cybersecurity awareness. The hypotheses were tested and analyzed following the quantitative research methodology through conducting appropriate statistical tests, such as correlations, t-tests, ANOVA, and regression. The independent variables were gender, age, education, seniority, and cyber compromising; the dependent variable was cybersecurity awareness. Data collection for the employee's group acquired online by Facebook. The sample includes 1033 active employees out of 9264 employees representing the study population. The results provided several pieces of evidence that online communications by young employees were more efficient than older ones, and cybersecurity awareness of males was better than females. Nevertheless, there was a strong correlation between seniority employees, and the effectiveness of their cybersecurity awareness, as well as the topology structure, had profound effects on the ability of the employees' group to fulfill their objectives. Employees with higher education had more influence than the others; but in contrast, there was no apparent difference between the influence of employees whereas compromised or non-compromised.

Background: Online social support groups have become a popular source of health information for patients and the comments exchanged there can be regarded as a kind of social support. Purpose: The aim of this qualitative study was to explore and categorize messages of social support exchanged by hysterectomy patients who visited http://www.hystersisters.com during a six-week period. Two questions were examined: (a) which types of social support are most frequently exchanged on hystersisters.com? and (b) in what ways do women exchange social support on hystersisters.com? Method: A total of 200 messages posted were selected for analysis. Using Cutruna and Suhr's coding framework and a grounded theory approach, messages were categorized within the coding framework and then analyzed to explore other emerging themes. Results: Findings revealed that informational and emotional support were exchanged most frequently, followed by esteem support, with network and tangible aid as the least frequently exchanged forms of support. Affection and relational support were the most frequently used sub-categories of social support overall, creating a unique sense of community that helped users to transcend their circumstances. Findings also revealed that the sharing of advice and encouragement through personal narratives promoted reciprocal self-disclosure and community-building. Conclusions: Further study should investigate which forms of social support are most effective in health-related online support groups and whether these forms of support result in feelings of self-efficacy, better interactions with health providers, and/or improved health outcomes. Recommendations for practitioners and suggestions for future research are provided.

In recent years, online social networks have become a part of everyday life for millions of individuals. Also, data analysts have found a fertile field for analyzing user behavior at individual and collective levels, for academic and commercial reasons. On the other hand, there are many risks for user privacy, as information a user may wish to remain private becomes evident upon analysis. However, when data is anonymized to make it safe for publication in the public domain, information is inevitably lost with respect to the original version, a significant aspect of social networks being the local neighborhood of a user and its associated data. Current anonymization techniques are good as identifying risks and minimizing them, but not so good at maintaining local contextual data which relate users in a social network. Thus, improving this aspect will have a high impact on the data utility of anonymized social networks. Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets. Hence, in the present work we address these issues by designing and implementing a sophisticated synthetic data generator together with an anonymization processor with strict privacy guarantees and which takes into account the local neighborhood when anonymizing. All this is done for a complex dataset which can be fitted to a real dataset in terms of data profiles and distributions. In the empirical section we perform experiments to demonstrate the scalability of the method and the improvement in terms of reduction of information loss with respect to approaches which do not consider the local neighborhood context when anonymizing.

- by David Nettleton
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- Data Mining, Online social networks, Graph Data Mining, Graph Mining

Online Social Networks (OSNs) are a fertile medium through which users can unleash their opinions and share their thoughts, activities and knowledge of various topics and domains. This medium allows legitimate users as well as spammers to publish their content, leveraging the open environment and fewer restrictions associated with OSNs. Hence, it is essential to evaluate users' credibility in various domains and accordingly make judgements about potentially influential users in a particular domain(s). Most of the existing trustworthiness evaluation approaches of users and their posts in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users' influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present a preliminary approach to evaluating domain-based user's trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user's content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated considering temporal factor. The approach presented in this paper is promising since it provides a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs.

- by Bilal Abu-Salih and +1
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- Information Retrieval, Semantic Analysis, Big Data, Online Social Networks (OSNs)

public web facility of Google Inc., based on Google Search, that shows how often a particular search-term is entered relative to the total search-volume across various regions of the world, and in various languages. The horizontal axis of the main graph represents time (starting from 2004), and the vertical is how often a term is searched for relative to the total number of searches, globally. Below the main graph, popularity is broken down by countries, regions, cities and language. Source: Wikipedia Beta: Measuring search interest in topics is a beta feature which quickly provides accurate measurements of overall search interest. To measure search interest for a specific query select the "search term" option.

- by Lisa Beck
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- New Media, Digital Media, Social Media, Islamic Studies

Twitter is one of the most influential Online Social Networks (OSNs), adopted not only by hundreds of millions of users but also by public figures, organizations, news media, and official authorities. One of the factors contributing to this success is the inherent property of the platform for spreading news --
encapsulated in short messages that are tweeted from one user to another -- across the globe. Today, it is sufficient to just inspect the trending topics in Twitter for figuring out what is happening around the world. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock-market crash for a short period of time in 2013. In this paper, we analyze a large collection of tweets and explore the dynamics of popular trends and other Twitter features in regards to deliberate misuse. We identify a specific class of trend-exploiting campaigns that exhibits a stealthy behavior and hides spam URLs within Google search-result links. We build a spam classifier for both users and tweets, and demonstrate its simplicity and efficiency. Finally, we visualize these spam campaigns and reveal their inner structure.

- by Despoina Antonakaki
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- Online Social Networks (OSNs), Spam Detection

The article is about the features of application of network Internet-communications for advancement of the goods. Wide development of Internet technologies has transformed social communications into the independent tool of marketing. Authors classify and analyze possibilities of use of network Internet-communications in the marketing environment.
В статье рассматриваются особенности применения сетевых коммуникаций для продвижения товаров в Интернет. Широкое развитие Интернет-технологий превратило социальные коммуникации в самостоятельный инструмент маркетинга. Авторы классифицируют и анализируют возможности использования сетевых интернет-коммуникаций в маркетинговой среде.

We present the design, implementation, and evaluation of Centaur, an application-level user-assisted message dissemination solution for Online Social Networks (OSN). Characteristics of OSNs make their message dissemination distinct from scenarios like multicast streaming and P2P file sharing. First, updates issued by each user are sporadic and the "online" follower set is highly dynamic. Hence, it is unnecessarily expensive to maintain always-alive multicast topologies. Second, the key advantage of OSNs over traditional media is realtime update, which would be greatly shadowed if it takes long to construct well-shaped dissemination structures. Therefore, in contrast to the multitude of prior multicast solutions, Centaur constructs location-aware dissemination trees locally for each incoming message. We implement a prototype with Cirrus and evaluate it with Twitter data. Experiment results show that Centaur achieves 98% delivery ratio and few seconds of delay with only around one tenth server traffic compared to centralized solutions used in many current OSNs.

- by Shen Li
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- Computer Science, Facebook, Twitter, Peer-to-Peer

Social media has millions of users across the industry. Customer interactions with these social sites, including Twitter and Facebook, have a tremendous impact and sometimes unwanted repercussions on everyday life. Prominent social media sites have become a target platform for spammers to spread large amounts of inappropriate and harmful information. For example, Twitter has ended up being one of the most used structures ever and allows for an unreasonable amount of unwanted email. Fake users send unwanted Tweets to users to sell offers or websites that now affect legitimate customers and disrupt the consumption of useful resources. Also, the possibility of increasing invalid customer data through fake identities increased the impact of spreading malicious content. In recent times, spotting spammers and fake customer identities on Twitter has become a common area of study in the latest online social network (OSN). This paper evaluates the strategies used to detect Twitter spammers and proposed Modified Random Forest (MRF) model. Also, a ranking of Twitter's spam detection methods is presented, which ranks strategies based on their potential to stumble upon: (1) fake content, (2) entire URL-based spam, (3) spam in current affairs and (4) Fake users. The presented strategies are also compared based on characteristics, content characteristics, graphics characteristics, structure functions, and time capabilities. We hope the Featured Note will be a useful and useful resource for researchers to find the most recent Twitter spam detection trends on one platform.

Online Social Networks (OSNs) Research Papers (original) (raw)