Traditional Online Social Networks Research Articles

Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND

There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as X (formerly known as Twitter) and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However, users in EESNs are naturally social stratified, consisting of both enterprise staffs and customers. In addition, the motivation to operate EESNs can be quite complicated, including providing customer services and facilitating communication among enterprise staffs. As a result, the social behaviors in EESNs can be quite different from those in OSNs and ESNs. In this work, we aim to analyze the social behaviors in EESNs. We consider the Chinese car manufacturer NIO as a typical example of EESNs and provide the following contributions. First, we formulate the social behavior analysis in EESNs as a link prediction problem in heterogeneous social networks. Second, to analyze this link prediction problem, we derive plentiful user features and build multiple meta-path graphs for EESNs. Third, we develop a novel Fast (H)eterogeneous graph (A)ttention (N)etwork algorithm for (D)irected graphs (FastHAND) to predict directed social links among users in EESNs. This algorithm introduces feature group attention at the node-level and uses an edge sampling algorithm over directed meta-path graphs to reduce the computation cost. By conducting various experiments on the NIO community data, we demonstrate the predictive power of our proposed FastHAND method. The experimental results also verify our intuitions about social affinity propagation in EESNs.

Toward Zero-Trust Security for the Metaverse

By focusing on immersive interaction among users, the burgeoning Metaverse can be viewed as a natural extension of existing social media. Similar to traditional online social networks, there are numerous security and privacy issues in the Metaverse (e.g., attacks on user authentication and impersonation). In this paper, we develop a holistic research agenda for zero-trust user authentication in social virtual reality (VR), an early prototype of the Metaverse. Our proposed research includes four concrete steps: investigating biometrics-based authentication that is suitable for continuously authenticating VR users, leveraging federated learning (FL) for protecting user privacy in biometric data, improving the accuracy of continuous VR authentication with multimodal data, and boosting the usability of zero-trust security with adaptive VR authentication. Our preliminary study demonstrates that conventional FL algorithms are not well suited for biometrics-based authentication of VR users, leading to an accuracy of less than 10%. We discuss the root cause of this problem, the associated open challenges, and several future directions for realizing our research vision.

The YouTube Social Network

Today, YouTube is the largest user-driven video content provider in the world; it has become a major platform for disseminating multimedia information. A major contribution to its success comes from the user-to-user social experience that differentiates it from traditional content broadcasters. This work examines the social network aspect of YouTube by measuring the full-scale YouTube subscription graph, comment graph, and video content corpus. We find YouTube to deviate significantly from network characteristics that mark traditional online social networks, such as homophily, reciprocative linking, and assortativity. However, comparing to reported characteristics of another content-driven online social network, Twitter, YouTube is remarkably similar. Examining the social and content facets of user popularity, we find a stronger correlation between a user's social popularity and his/her most popular content as opposed to typical content popularity. Finally, we demonstrate an application of our measurements for classifying YouTube Partners, who are selected users that share YouTube's advertisement revenue. Results are motivating despite the highly imbalanced nature of the classification problem.

Responses to mass shooting events

Research SummaryPublic mass shootings tend to capture the public's attention and receive substantial coverage in both traditional media and online social networks (OSNs) and have become a salient topic in them. Motivated by this, the overarching objective of this paper is to advance our understanding of how the public responds to mass shooting events in such media outlets. Specifically, it aims to examine whether distinct information seeking patterns emerge over time and space, and whether associations between public mass shooting events emerge in online activities and discourse. Towards this objective, we study a sequence of five public mass shooting events that have occurred in the United States between October 2017 and May 2018 across three major dimensions: the public's online information seeking activities, the media coverage, and the discourse that emerges in a prominent OSN. To capture these dimensions, respectively, data was collected and analyzed from Google Trends, LexisNexis, Wikipedia Page views, and Twitter. The results of our analysis suggest that distinct temporal patterns emerge in the public's information seeking activities across different platforms, and that associations between an event and its preceding events emerge both in the media coverage and in OSNs.Policy ImplicationStudying the evolution of discourse in OSNs provides a valuable lens to observe how society's views on public mass shooting events are formed and evolved over time and space. The ability to analyze such data allows tapping into the dynamics of reshaping and reframing public mass shooting events in the public sphere and enable it to be closely studied and modeled. A deeper understanding of this process, along with the emerging associations drawn between such events, can then provide policy and decision‐makers with opportunities to better design policies and communicate the significance of their goals and objectives to the public.

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years. They necessitate fundamental social operations such as establishing friend relationship, location sharing among friends, and providing location-based services. As a consequence, security and privacy issues demands the utmost importance to mOSNs users. The first stream of existing solutions adopts two different servers to store locations-based and social network-based information separately, thereby sustaining large storage and communication overhead. The second stream of solutions aims at integrating the social network server and the location-based server into a single entity. However, as these approaches exploit only one single server, they may face several performance issues related to server bottlenecks. Moreover, such schemes are found to be vulnerable to various active and passive security attacks. In this paper, we propose a privacy preserving, secure and efficient location sharing scheme for mOSNs, which shows both efficiency and flexibility in the location update, sharing, and query of social friends and social strangers. The security of the proposed scheme is validated using random oracle based formal security proof and Burrows-Abadi-Needham (BAN) logic based authentication proof, followed by informal security analysis. Additionally, we have used ProVerif 1.93 to verify the security of the system. The efficiency and practicability of the proposed scheme are demonstrated through experimental implementation and evaluation.

Engagement on online social networks during a social crisis: effect of anonymity level

Recent social movements have increasingly used online social networks (OSNs) to spread information and organise activists. Unfortunately, using an OSN during a social crisis also allows for uncertainties and rapid dissemination of misinformation. During the fall of 2015, a student activist group protested and received nationwide attention. At the time, two OSNs were very popular: Twitter, a traditional OSN, and Yik Yak, an anonymous OSN. In this research, we performed a sentiment analysis on datasets from each of these platforms during the social crisis. Our findings suggest that the two platforms are used differently during a social crisis. Posts on traditional OSNs receive more engagement if they have a high total sentiment, but are not overly positive or negative. Anonymous OSN posts see increased engagement with longer messages that are positively or negatively charged. These findings indicate a need for divergent theorising for anonymous OSNs.

SOLVER: A Framework for the Integration of Online Social Networks with Vehicular Social Networks

Online social networks (OSNs) are comprised of people with social interactions and relationships, such as friendship and acquaintance. Social web communities (e.g., Facebook or Twitter), and content-sharing sites with social networking functionality (e.g., YouTube), have attracted millions of users, and OSNs are continuing to grow, with a giant number of users. At the same time, new social services are emerging, moving social networking from traditional OSNs toward vehicular social networks (VSNs). VSNs consider a social network where every vehicle is capable of establishing social ties in an autonomous way with other vehicles or drivers/passengers. However, limitations exist in VSNs, such as reduced connectivity due to mobility models and the opportunistic nature of vehicular networks. Similar considerations apply in OSNs, where the centralized architecture may suffer from high traffic load. In this article, the integration of OSNs with VSNs is realized through a hybrid framework that merges the centralized structure of OSNs with the distributed opportunistic nature of VSNs. The proposed hybrid OSN-VSN framework, named SOLVER, allows communications both among different VSNs and toward OSN communities, in order to maintain connectivity and service that can be affected by mobility. Connectivity holes of VSNs can be limited through a handover procedure toward an OSN community, and traffic load in an OSN can be redirected to specific VSNs in order to release network resources in OSNs, with the consequent reduction of transmission delay. SOLVER presents a solution for maintaining connectivity in VSNs and extending it toward OSNs, and vice versa. Security and privacy issues are also addressed through the use of a multi-layer security approach adopted in SOLVER. As a main strength, SOLVER is able to synergistically exploit the positive features of OSNs and VSNs, as well as providing security and privacy, with mechanisms that are completely agnostic to the final users.

Fake reviews tell no tales? dissecting click farming in content-generated social networks

Recently, there has been a radial shift from traditional online social networks to content-generated social networks (CGSNs). Contemporary CGSNs, such as Dianping and TripAdvisor, are often the targets of click farming in which fake reviews are posted in order to boost or diminish the ratings of listed products and services simply through clicking. Click farming often emanates from a collection of multiple fake or compromised accounts, which we call click farmers. In this paper, we conduct a three-phase methodology to detect click farming. We begin by clustering communities based on newly-defined collusion networks. We then apply the Louvain community detection method to detecting communities. We finally perform a binary classification on detected-communities. Our results of over a year-long study show that (1) the prevalence of click farming is different across CGSNs; (2) most click farmers are lowly-rated; (3) click-farming communities have relatively tight relations between users; (4) more highly- ranked stores have a greater portion of fake reviews.

Distributed Multi-authority Attribute-based Encryption Scheme for Friend Discovery in Mobile Social Networks

In recent years, the rapid expansion of the capability of portable devices, cloud servers and cellular network technologies is the wind beneath the wing of mobile social networks. Compared to traditional web-based online social networks, the mobile social networks can assist users to easily discover and make new social interaction with others. A challenging task is to protect the privacy of the users’ profiles and communications. Existing works are mainly based on traditional cryptographic methods, such as homomorphic and group signatures, which are very computationally costly. In this paper, we propose a novel distributed multi-authority attribute-based encryption scheme to efficiently achieve privacy-preserving without additional special signatures. In addition, the proposed scheme can achieve fine-grained and flexible access control. Detailed analysis demonstrates the effectiveness and practicability of our scheme.

Feel Bored? Join Verse! Engineering Vehicular Proximity Social Networks

Travelers in vehicles often have a strong willingness to share their travel experience and exchange information to each other through social networks, such as Facebook and Twitter. This, however, can be costly due to the limited connections on the road to the Internet. In this paper, we develop Verse to facilitate the social communications among vehicle travelers on highways. Verse enables passengers onboard vehicles to share the content information, such as travel blogs with pictures, among each other using impromptu wireless intervehicle communications. Unlike traditional online social networks, which are built upon reliable internet protocol (IP) networks, vehicular social networks face fundamental challenges in that 1) users are anonymous and strangers to each other and that it is hard to identify potential friends of shared interests and that 2) users communicate through intermittent and unreliable intervehicle connections. On addressing the two challenges, Verse implements a friend recommendation function, which helps passengers efficiently identify potential social friends with both shared interests and relatively reliable wireless connections. In addition, Verse is equipped with a social-aware rate control scheme toward efficient utilization of network bandwidth. Using extensive simulations, we show that the friend recommendation function of Verse can effectively predict the mobility of vehicles to assist in social communication and that the social-aware rate control scheme quickly and efficiently adapts to the vehicle's transmission rate according to their social impact.

Improving MAC layer association through social-based metrics in mobile networks

Mobile Social Network (MSN) is a social network where one or more elements share interests or commonalities by connecting with one another using mobile devices. However, contrary to traditional online social networks, efficient communication and information exchanged depends on mobile users' context. In order to increase the scalability of the information exchange, network nodes need to be associated and grouped through an efficient set of rules and criterions, to minimize the time and amount of data messages flowing in the network. This article shows how social-based metrics can increase the efficiency of layer 2 associations in mobile networks, where nodes are able to join the network through their context information and probable interactions, optimizing the information exchange at MAC layer. Through a case study of mobile communications with different mobility patterns and dissemination approaches, we show that the social-based metrics increase overall network efficiency: maximize the percentage of information delivery, decrease the time to disseminate data traffic and reduce network usage.

Connected online and offline safe social networking for children

Children today are more connected and active on the Internet. Their extensive use of online social networks, which ultimately could have perplexing effects on the recognition of their identity and personality, has caused grave social concerns. In addition there is a lack of actual physical communication among children due to their preoccupation with the online self. To address these issues, an interactive robotic toy called Petimo was developed. We believe this introduction of new physical dimension improves safety and enjoyment of children in the social networking arena. As a proof-of-concept, a 3D virtual world called Petimo World is developed. It incorporates basic features of traditional online social networks to provide childern collaborative interaction and enjoyment with their friends. The concept of Petimo could be extended to any social network to create childerns applications to promote physical interaction and enhance enjoyment and safety issues in social networks.