Modern Online Social Networks Research Articles

Online Spreading of Topic Tags and Social Behavior

This article explores information spreading in modern online social networks. According to the law of information spread in real social networks, information retweeting is divided into two types: topic retweeting and relationship retweeting. Social behavior is considered as a higher order interaction, and the nodal influence effect in the traditional approach is abstracted as part of it for analysis. The process of topic communities being subjected to social behavior is simulated by the social behavior model, and the dynamic retweeting rate is established. A network evolution model is constructed based on the centrality and noncontinuity characteristics of topic communities in the spread process. The social reinforcement effect in information spreading is described in two dimensions by defining topic expansion rate and topic diffusion rate. This work conducts multiple views of analysis and visualization, which provide more results of quantitative aspect. The validity of the model is verified by comparing the model simulation results with real cases and the generalization ability experiments.

Analyzing user migration in blockchain online social networks through network structure and discussion topics of communities on multilayer networks

User migration, i.e. the movement of large sets of users from one online social platform to another one, is one of the main phenomena occurring in modern online social networks and even involves the most recent alternative paradigms of online social networks, such as blockchain online social networks (BOSNs). In these platforms, user migration mainly occurs through hard forks of the supporting blockchain, i.e. a split of the original blockchain and the creation of an alternative blockchain, to which users may decide to migrate. However, our understanding of user migration and its mechanisms is still limited, particularly regarding the role of densely connected user groups (communities) during migration and fork events. Are there differences between users who stay and those who decide to leave, in terms of network structure and discussion topics? In this work, we show, through network-based analysis centered on the identification of communities on multilayer networks and text mining that a) the “position” of a group within the network of social and economic interactions is connected to the likelihood of a group to migrate, i.e. marginal groups are more likely to leave; b) group network structure is also important, as users in densely connected groups interacting through monetary transactions are more likely to stay; c) users who leave are characterized by different discussion topics; and d) user groups interacting through monetary transactions show interest in migration-related content if they are going to leave. These findings highlight the importance of social and economic relationships between users during a user migration caused by fork events In general, in the larger context of online social media, it motivates the need to investigate user migration through a network-inspired approach based on groups and specific subgraphs while leveraging user-generated content, at the same time.

The role of degree correlation in shaping filter bubbles in social networks

Filter bubbles shelter people from unconcerned but important information, which is a critical problem in modern online social networks. Although a quantitative model of filter bubbles is still missing, the identification and impact of filter bubbles are widely debated both at a scientific and political level. To shed light on this gap, we introduce a theoretical directed network model of filter bubbles with degree correlations and mathematically analyze information diffusion dynamics on the model. We find that the internal structure of filter bubbles can be modeled by the directed scale-free network with both negative (a node tend to possess high in-degree and low out-degree, or vice versa) and assortative (two nodes with similar degrees tend to be connected) degree correlation. Traditionally, filter bubbles are usually associated with the community structure and emphasize the sparseness of external connections to isolate the spreading of diverse information. However, the negative-assortative degree correlation shows that the filter bubble can spontaneously resist the spreading of non-preferred information (i.e., information with relatively lower transmissibility). Moreover, we study the competition epidemic of two information on the negative-assortative networks, and find that both of the information can coexist only if all nodes prefer the same information.

Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion

By now people’s opinions and actions are more and more strongly influenced by what is posted and shared on the various social networks. Thus, malicious users can purposely manipulate other users posting fake news/reviews. In order to face this challenge, modern online social networks are beginning to adopt tool for user trustworthiness assessment. Current assessment solutions mainly adopt multi-criteria frameworks for user trustworthiness assessment but fail at properly dealing with uncertainty and vagueness in computed/collected scores and aggregating them in a robust manner. In this paper, we propose a larger set of criteria than existing related works, and the use of subjective logic to represent and combine subjective and objective scores. Specifically, several of assessment criteria are introduced for verifying user trust from different point of views (usefulness and quality of user reviews, users’ influence/importance in terms of activities and centrality within the social network, time dependent crown consensus investigating aspect-based sentiments and opinions of reviews w.r.t. the majority), aiming at improving accuracy and precision in trust estimation. The available fusion operators in the literature of subjective logic have been compared so as to find the best one fitting the needs of trust estimation. The proposed solution has been implemented and evaluated against public Yelp data-sets so as to prove its effectiveness and efficiency w.r.t. existing related works within the literature.

A survey about community detection over On-line Social and Heterogeneous Information Networks

In modern Online Social Networks (OSNs), the need to detect users’ communities based on their interests and social connections has became a more and more important challenge in literature. Community Detection supports and make more effective and efficient several Social Network Analysis (SNA) applications: the diffusion of a new idea or technologies can be maximized by identifying of people group interested about a given topic, the recommendation suggestion can be improved taking in account also how the social ties can be influenced the user chooses and the behaviors of people in the same communities, expert finding tasks could be more accurate if users are earlier subdivided into thematic groups, and so on. This paper presents a survey that provides a comprehensive and comparative study of all the different community detection techniques applicable to the various models proposed for OSNs. In particular, the most diffused approaches based on game theory, artificial intelligence and fuzzy strategies are detailed and compared, highlighting the related pros and cons. In addition, the problem of their applicability on the different OSN models is discussed, focusing on complex networks. Finally, the main open issues and challenges for the community detection problem are reported to address the futures work concerning this topic.

Evaluation of the Likelihood of Friend Request Acceptance in Online Social Networks

Recent years online social networks (OSNs) have become an essential digital platform in the daily life of billions of earth inhabitants. Despite the advantages of easy communication and information sharing, OSNs users often fell in trouble causing by security breaches and violations. A recurring example of the troubles arises due to a rash acceptance of the friendship request, which can lead to the disclosure of personal information and vulnerability to an attack. Support for making a secure friendship decision is limited in the modern OSNs, making their use hazardous especially for the groups of children and young people. To overcome this issue, the paper proposes a method for evaluating the likelihood to become a friend in support of promoting hazard-free cyber environments. The proposed approach allows a user to define a model of a friend-to-be, and incoming friend requests are evaluated with reference to this model. The model takes into account the attributes (like common interests) and the behavioral properties of a friend-to-be (like frequency of the posts). The method allows for filtering friend requests to the given users and triggering notification of anomalous behaviors in an OSN. An empirical study proves the validity of the proposed model and its favorable characteristics against existing methods in the current OSN platforms.

A hierarchy of linear threshold models for the spread of political revolutions on social networks

We study a linear threshold agent-based model (ABM) for the spread of political revolutions on social networks using empirical network data. We propose new techniques for building a hierarchy of simplified ordinary differential equation (ODE) based models that aim to capture essential features of the ABM, including effects of the actual networks, and give insight in the parameter regime transitions of the ABM. We relate the ABM and the hierarchy of models to a population-level compartmental ODE model that we proposed previously for the spread of political revolutions [1], which is shown to be mathematically consistent with the proposed ABM and provides a way to analyze the global behaviour of the ABM. This consistency with the linear threshold ABM also provides further justification a posteriori for the compartmental model of [1]. Extending concepts from epidemiological modelling, we define a basic reproduction number $R_0$ for the linear threshold ABM and apply it to predict ABM behaviour on empirical networks. In small-scale numerical tests we investigate experimentally the differences in spreading behaviour that occur under the linear threshold ABM model when applied to some empirical online and offline social networks, searching for quantitative evidence that political revolutions may be facilitated by the modern online social networks of social media.

A Model for Scale-Free Networks: Application to Twitter

In the last few years, complex networks have become an increasingly relevant research topic due to the large number of fields of application. Particularly, complex networks are especially significant in the area of modern online social networks (OSNs). OSNs are actually a challenge for complex network analysis, as they present some characteristics that hinder topology processing. Concretely, social networks’ volume is exceedingly big, as they have a high number of nodes and links. One of the most popular and influential OSNs is Twitter. In this paper, we present a model to describe the growth of scale-free networks. This model is applied to Twitter after checking that it can be considered a “scale-free” complex network fulfilling the small world property. Checking this property involves the calculation of the shortest path between any two nodes of the network. Given the difficulty of this computation for large networks, a new heuristic method is also proposed to find the upper bounds of the path lengths instead of computing the exact length.

Online social network contacts as information repositories

Normal 0 21 false false false CA X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0pt 5.4pt 0pt 5.4pt; mso-para-margin:0pt; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Les xarxes socials han existit des de l'antiguitat. Amb l'avanç tecnològic, han evolucionat cap a les modernes xarxes socials en línia. L'explosió d'aquestes darreres ha anat acompanyada d'importants impactes socials, incloent-hi el de l'à mbit educatiu. Ara un gran nombre d'estudiants universitaris pertanyen a alguna xarxa social i hi passen unes quantes hores a la setmana. Alguns sectors de la societat, com pares de família i professors, estan preocupats per l'impacte negatiu que això pot tenir tant en la seva activitat acadèmica com en la seva vida personal. Tanmateix, segons el connectivisme de George Siemens, els contactes d'una xarxa social en línia representen una font d'informació potencial i valuosa. En aquest estudi es va buscar identificar els factors que afavoreixen que un contacte d'una xarxa social en línia es converteixi en una font d'informació, davant una iniciativa d'aprenentatge. S'hi presenta una investigació, de naturalesa qualitativa, que es va fer en una institució privada d'ensenyament superior, amb un grup de vint-i-un estudiants acabats de sortir d'una carrera en particular i tretze professors universitaris. Els resultats demostren que els factors que afavoreixen que un contacte es converteixi en font d'informació en una xarxa social en línia són aquests: que es tingui coneixement sobre el contacte, que se sà piga el que el contacte sap, que es tingui proximitat social, que el contacte tingui un cert prestigi, que es conegui el contacte en persona i que sigui accessible.