Model Of Online Social Network Research Articles

The revenue model of mainstream online social networks and potential alternatives: A scenario-based evaluation by German adolescents and adults

Mainstream Online Social Networks (OSNs) face extensive criticism for their revenue model and its negative consequences for users and societies. While experts often discuss alternative OSN models, little is known about potential users’ evaluations of different OSN models. To close this research gap, the present study investigates how non-expert individuals, i.e., potential users, evaluate mainstream OSNs and expert-proposed alternative models. Next to the general evaluations of different models, individual differences among users open to various OSN models were explored.Three OSN scenarios were created based on interviews with experts from academia and industry and presented to samples of German adolescents (N = 1166) and adults (N = 878): one describing the “status quo”, one “option” model similar to the model currently considered by Meta, and one describing a “public-service” broadcasting OSN. Participants rated each characteristic of the “status quo” scenario, indicated their willingness to pay for each of the three OSN models, and specified their preferred OSN model next to providing sociodemographic information.Replicated across both samples was that the “status quo” scenario received predominantly negative evaluations. Further replicated across both samples was that most participants were willing to pay for a “public-service” OSN and chose this model as the preferred one. Only a few significant relations of sociodemographic variables were observed. Consistently replicated across both samples, men rated various characteristics of the “status quo” scenario more positively, and women were more likely to prefer the “public-service” OSN. Some differences between the two samples also emerged.In summary, both adolescents and adults demonstrated receptivity to alternative OSN models, especially a public-service broadcasting OSN. Most sociodemographic factors had limited influence indicating potential widespread adoption if such an alternative was implemented – at least in Germany. Consequences arising from such a model for platform design, policy, regulation, and governance are discussed.

A novel spam detection technique for detecting and classifying malicious profiles in online social networks

Online social networks (OSNs) are utilized by millions of people from the entire world to communicate with others through Facebook and Twitter. The removal of fake accounts will increase the efficiency of the protection in OSNs. The construction of the OSN model has the nodes and the links to identify the fake profiles on Twitter. This paper proposes a novel technique to detect spam profiles and the proposed classifier is to classify the profile images from the dataset. The malicious profile detection technique is used to identify the fake profiles with the concept of a Twitter crawler that implements the extraction of data from the profile. The feature set analysis has been implemented with the feature related analysis. The user behavior detection utilizes the adjacent matrix to measure the similarity values within the friend’s profiles. The multi-variant Support Vector Machine classifier is developed for efficient classification with the kernel function. The proposed technique is compared with the well-known techniques of ECRModel, ISMA and DeepLink that the detection rate is 2.5% higher than the related techniques, the computation time is 220 s lesser than the related techniques and the proposed technique has 3.1% higher accuracy.

Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. Some works propose to let people become friends if they have similar profile attributes. However, profile matching involves an inherent privacy risk of exposing private profile information to strangers in the cyberspace. The existing solutions to the problem attempt to protect users’ privacy by privately computing the intersection or intersection cardinality of the profile attribute sets of two users. These schemes have some limitations and can still reveal users’ privacy. In this paper, we leverage community structures to redefine the OSN model and propose a realistic asymmetric social proximity measure between two users. Then, based on the proposed asymmetric social proximity, we design three private matching protocols, which provide different privacy levels and can protect users’ privacy better than the previous works. We also analyze the computation and communication cost of these protocols. Finally, we validate our proposed asymmetric proximity measure using real social network data and conduct extensive simulations to evaluate the performance of the proposed protocols in terms of computation cost, communication cost, total running time, and energy consumption. The results show the efficacy of our proposed proximity measure and better performance of our protocols over the state-of-the-art protocols.

A new access control scheme for Facebook-style social networks

The popularity of online social networks (OSNs) makes the protection of users’ private information an important but scientifically challenging problem. In the literature, relationship-based access control schemes have been proposed to address this problem. However, with the dynamic developments of OSNs, we identify new access control requirements which cannot be fully captured by the current schemes. In this paper, we focus on public information in OSNs and treat it as a new dimension which users can use to regulate access to their resources. We define a new OSN model containing users and their relationships as well as public information. Based on this model, we introduce a variant of hybrid logic for formulating access control policies. We exploit a type of category information and relationship hierarchy to further extend our logic for its usage in practice. In the end, we propose a few solutions to address the problem of information reliability in OSNs, and formally model collaborative access control in our access control scheme.

A Theoretic Extension and Empirical investigation of conducting Business Online Social Network: The Continuance Intention Phenomenon

Online Social Network (OSN) is a web 2.0 enabled technology that permits OSN participants to interact with both old and new friends initially. This model of OSN ventured into conducting business activities on platforms, which resulted in many springing up but not surviving, yet the explosion of business activities on these platforms continuous to grow. It is therefore important that OSN practitioners and researchers understand the key determinants of OSN business transaction and continuance intention propellants. The purpose of this paper is to establish the factors that determine OSN participant’s continuance intention to do business on OSN platform. The research framework is grounded in an extended expectation-confirmation model (ECM). An online survey model was used to collect 300 valid responses from OSN participants who have ever conducted business using OSN. A partial least square version 2.0.M3 (PLS) and Warp PLS 4.0 were deployed to perform CFA analyses and structural equation modelling, respectively. The emerging results provide significant evidence in support of the five out of nine factors tested against the hypotheses proposed, namely: Perceived Behavioural Control (0.01), Satisfaction (0.14), Expected Benefit (0.15), Social Norms (0.24), and Habit (0.31), as the main determinants of OSN continuance intention.

Geometric Protean Graphs

We study the link structure of online social networks (OSNs) and introduce a new model for such networks that may help in inferring their hidden underlying reality. In the geo-protean (GEO-P) model for OSNs, nodes are identified with points in Euclidean space, and edges are stochastically generated by a mixture of the relative distance of nodes and a ranking function. With high probability, the GEO-P model generates graphs satisfying many observed properties of OSNs, such as power-law degree distributions, the small-world property, densification power law, and bad spectral expansion. We introduce the dimension of an OSN based on our model and examine this new parameter using actual OSN data. We discuss how the geo-protean model may eventually be used as a tool to group users with similar attributes using only the link structure of the network.