Online Social Networks: Threats and Solutions Fire, Michael; Goldschmidt, Roy; Elovici, Yuval
IEEE Communications surveys and tutorials,
01/2014, Volume:
16, Issue:
4
Journal Article
Peer reviewed
Open access
Many online social network (OSN) users are unaware of the numerous security risks that exist in these networks, including privacy violations, identity theft, and sexual harassment, just to name a ...few. According to recent studies, OSN users readily expose personal and private details about themselves, such as relationship status, date of birth, school name, email address, phone number, and even home address. This information, if put into the wrong hands, can be used to harm users both in the virtual world and in the real world. These risks become even more severe when the users are children. In this paper, we present a thorough review of the different security and privacy risks, which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users. We also offer simple-to-implement recommendations for OSN users, which can improve their security and privacy when using these platforms. Furthermore, we suggest future research directions.
The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of ...technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.
•A review of novel and popular online social network analysis problems has been presented.•Related applications and a reference work for researchers interested in analyzing online social network data and social network problems have been examined.•Unlike other individual studies 21 online social network problems have been gathered and defined with related studies for the first time.
We review the state of the art of privacypreserving schemes for ad hoc social networks including mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and ...examine in-detail 33 privacy-preserving schemes developed for or applied in the context of ad hoc social networks. Based on novel schemes published between 2008 and 2016, we survey privacy preservation models including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. Recent significant attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in the form of tables. In addition, an overview of recommendations for further research is provided. With this survey, readers can acquire a thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks.
Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, ...cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users' content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.
Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently ...simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents' opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorously examine stability properties of the proposed model, in particular, convergence of the agents' opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached "on average" via asynchronous gossip-based protocols.
Social networks on the Internet Musiał, Katarzyna; Kazienko, Przemysław
World wide web (Bussum),
2013/1, Volume:
16, Issue:
1
Journal Article
Peer reviewed
Open access
The rapid development and expansion of the Internet and the social–based services comprised by the common Web 2.0 idea provokes the creation of the new area of research interests, i.e. social ...networks on the Internet called also virtual or online communities. Social networks can be either maintained and presented by social networking sites like
MySpace
,
LinkedIn
or indirectly extracted from the data about user interaction, activities or achievements such as emails, chats, blogs, homepages connected by hyperlinks, commented photos in multimedia sharing system, etc. A social network is the set of human beings or rather their digital representations that refer to the registered users who are linked by relationships extracted from the data about their activities, common communication or direct links gathered in the internet–based systems. Both digital representations named in the paper internet identities as well as their relationships can be characterized in many different ways. Such diversity yields for building a comprehensive and coherent view onto the concept of internet–based social networks. This survey provides in–depth analysis and classification of social networks existing on the Internet together with studies on selected examples of different virtual communities.
With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with ...cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
•Explores connections and patterns created by the aggregated interactions in Facebook pages during disaster responses.•Analyzes social media data from the Facebook page of city of Baton Rouge during ...the 2016 Louisiana flood (Aug 12–Dec 1, 2016).•Analyzes social roles and key players using social network analysis.•Study recommends actions to improve the effectiveness of information diffusion via social media.
Social media, such as Twitter and Facebook, plays a critical role in disaster management by propagating emergency information to a disaster-affected community. It ranks as the fourth most popular source for accessing emergency information. Many studies have explored social media data to understand the networks and extract critical information to develop a pre- and post-disaster mitigation plan.
The 2016 flood in Louisiana damaged more than 60,000 homes and was the worst U.S. disaster after Hurricane Sandy in 2012. Parishes in Louisiana actively used their social media to share information with the disaster-affected community − e.g., flood inundation map, locations of emergency shelters, medical services, and debris removal operation. This study applies social network analysis to convert emergency social network data into knowledge. We explore patterns created by the aggregated interactions of online users on Facebook during disaster responses. It provides insights to understand the critical role of social media use for emergency information propagation. The study results show social networks consist of three entities: individuals, emergency agencies, and organizations. The core of a social network consists of numerous individuals. They are actively engaged to share information, communicate with the city of Baton Rouge, and update information. Emergency agencies and organizations are on the periphery of the social network, connecting a community with other communities. The results of this study will help emergency agencies develop their social media operation strategies for a disaster mitigation plan.
Many studies have found a link between time spent using social media and mental health issues, such as depression and anxiety. However, the existing research is plagued by cross-sectional research ...and lacks analytic techniques examining individual change over time. The current research involves an 8-year longitudinal study examining the association between time spent using social media and depression and anxiety at the intra-individual level. Participants included 500 adolescents who completed once-yearly questionnaires between the ages of 13 and 20. Results revealed that increased time spent on social media was not associated with increased mental health issues across development when examined at the individual level. Hopefully these results can move the field of research beyond its past focus on screen time.
•Time spent using social media was not related to individual changes in depression or anxiety over 8 years.•This lack of a relationship was found even in the transition between adolescence and emerging adulthood.•Results were not stronger for girls or boys.
Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a ...crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.
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