Structure Of Online Social Networks Research Articles

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

Interpreting Graph-Based Sybil Detection Methods as Low-Pass Filtering

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

Network Amplification of Politicized Information and Misinformation about COVID-19 by Conservative Media and Partisan Influencers on Twitter

ABSTRACT Social media amplification is both a mechanism to attract public attention and a process of information diffusion shaped by the online social network structure. This study focuses on amplification by elites on social media and examines the extent to which traditional media and emerging partisan influencers engage in “network amplification.” Defined as like-minded elites sharing similar or/and mutual messages, network amplification highlights the interrelation and interaction between elite messages in the network communication environment of social media. This is a phenomenon worth investigating because network amplification’s resulting message repetition and reinforcement can multiply the overall effectiveness of elite messaging. Using network sampling and spectral clustering, we collected 358,707 accounts that followed 2,069,311 accounts on Twitter and detected nine distinct networks of traditional media and emerging partisan influencers. We then examined their 3,540,629 tweets related to the COVID-19 pandemic. Results show that 1) conservative media and influencers engaged in network amplification of politicized information and misinformation significantly more than liberal media and influencers did; 2) conservative influencers exhibited a stronger tendency to retweet and align their messages with conservative media than liberal influencers did regarding liberal media; and 3) traditional media partially drove partisan influencers’ amplification. The implications of network amplification for partisan asymmetry, misinformation, and public opinion are discussed.

Social network analysis and applications: A review of the broad research aspects of social network structure

The use of social media has generated huge Online Social Networks (OSN). This network exhibits nice structural properties and has opened door to various research tasks like community detection, link prediction and influence modeling in OSN. There are several models such as linear threshold and independent cascade model proposed in the literature that models the influence diffusion process. Depending on the use-case one may want to amplify or attenuate information in OSN called influence maximization (IM) and rumor blocking (RB), respectively. In terms of marketing, it is called viral marketing. This paper extensively reviews the fundamental research tasks associated with the structure of OSN, groups them into different categories based on the fundamental research area discussed along with its applications. This paper also surveys RB and the various variants of IM found in the literature.

SybilHunter: Hybrid graph-based sybil detection by aggregating user behaviors

Nowadays, online social networks (OSNs) support users in sharing their information and providing valuable data for various applications, e.g., rating and recommendation systems, search engine systems, etc. In such online social network-based applications, information quality is essential. However, the easy-to-use interactive user interfaces and free publication mechanisms facilitate malicious users to launch Sybil Attacks. They create fake identities, pollute user-generated content, and cause server information quality problems, such as buzz, rumor, spam, etc. Most existing graph-based sybil detection approaches only consider the static graph structure features and heavily rely on the prior knowledge of labeled nodes. “Limited-attack-edge” assumption, the critical assumption of these approaches has been proved invalid in many scenarios.In this paper, we propose SybilHunter, a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes’ similarity according to the dynamic user behavior features to evaluate user pairs’ trustworthiness and consistency. Then it constructs a weighted-strong-social (WSS) graph, based on which SybilHunter outputs sybil nodes. We simulate and evaluate our approach under a Weibo dataset. The AUC of SybilHunter achieves 0.945, which is significantly higher than typical graph-based sybil detection methods, such as SybilRank, SybilWalk, etc.

Reconstructing community structure of online social network via user opinions.

User opinion affects the performance of network reconstruction greatly since it plays a crucial role in the network structure. In this paper, we present a novel model for reconstructing the social network with community structure by taking into account the Hegselmann-Krause bounded confidence model of opinion dynamic and compressive sensing method of network reconstruction. Three types of user opinion, including the random opinion, the polarity opinion, and the overlap opinion, are constructed. First, in Zachary's karate club network, the reconstruction accuracies are compared among three types of opinions. Second, the synthetic networks, generated by the Stochastic Block Model, are further examined. The experimental results show that the user opinions play a more important role than the community structure for the network reconstruction. Moreover, the polarity of opinions can increase the accuracy of inter-community and the overlap of opinions can improve the reconstruction accuracy of intra-community. This work helps reveal the mechanism between information propagation and social relation prediction.

Increase of low-frequency modes of user dynamics in online social networks during overheating of discussions

User dynamics in online social networks have a significant impact on not only the online community but also real-world activities. As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of social networks. However, since the oscillation model was introduced as a purely theoretical model of social networks, it is necessary to confirm whether the model describes real phenomena correctly or not. In this paper, we first show a prediction from the oscillation model; the low-frequency oscillation mode of user dynamics will be dominant when the structure of online social networks changes so that user activity is activated. To verify the predictions with actual data, we show spectral analyses of both the log data of posts on an electronic bulletin board site and the frequency data of word search from Google Trends. The results support the predictions from the theoretical model.

Derivation and characteristics of closed‐form solutions of the fundamental equations for online user dynamics

SummaryThe oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of the link structure of online social networks, a novel fundamental equation of different forms has been devised. In this article, we derive a closed‐form solution of the new fundamental equation. Also, we show that the closed‐form solution of the new fundamental equation can generate the general solution of the original wave equation and investigate the characteristics of the derived general solution.

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

In response to the lack of research on the online social network structure of athletes, elements of research on the online social network structure of athletes were constructed based on the whole network perspective and through the study of the characteristics of the whole online social network structure of athletes, in order to provide reference for the physical and mental health development of athletes from a new perspective. Data were collected through questionnaires, and several software programs were used to preprocess and analyse the collected data. Through the analysis of the online whole network structure, it was found that the network density of the online support network was generally greater than that of the online discussion network, and athletes still showed stronger practical support demands and behaved more rationally in the process of training and learning life, while from the perspective of the relationship structure, the athletes’ family and classmates’ online support is weaker than that in previous studies; in terms of the whole network, strong relationships still dominate in this population, while attention should be paid to the impact of weak relationships.

Hybrid graph-based Sybil detection with user behavior patterns

Online social networks (OSNs) are known to be vulnerable to Sybil Attack, where attackers leverage the openness to create multiple fake identities for launching many malicious activities. In this paper, we define a weighted-strong-social (WSS) graph that integrates the OSN structure and user behavior patterns and propose a novel hybrid graph-based sybil detection approach. The hybrid approach estimates the trustworthiness of users and user pairs based on user behaviors that can be obtained locally and add them to the OSN structure to construct a WSS graph for sybil detection. The evaluation results show that the AUC of the hybrid approach is 0.954, which is significantly higher than that of previous sybil detection methods.

Social influence based community detection in event-based social networks

In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm.

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.

5. Estimation of the Structure of Online Social Networks by Using the Compressed Sensing

5. Estimation of the Structure of Online Social Networks by Using the Compressed Sensing

"Everybody Puts Their Whole Life on Facebook": Identity Management and the Online Social Networks of LGBTQ Youth.

Lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth and young adults almost inevitably “come out”, or self-disclose their identity to others. Some LGBTQ youth are more uniformly “out”, while others may disclose to some groups but not others. This selective disclosure is complicated on real name social media sites, which tend to encourage a unified presentation of self across social contexts. We explore these complications with a cohort of LBGTQ youth on Facebook (N = 199, Mage = 24.13). Herein we ask: How do LBGTQ youth manage the disclosure of their sexual orientation and/or gender identity to different people in their lives? Further, are there identifiable differences in the online social network structure for LGBTQ youth who manage outness in different ways? Finally, how do LGBTQ young people describe their experiences on Facebook? We answer these questions using a mixed methods approach, combining statistical cluster analysis, network visualization, and qualitative data. Our findings illustrate patterns in network structure by outness cluster type, highlighting both the work involved in managing one’s online identity as well as the costs to (semi-) closeted individuals including a considerably lower overall network connectivity. In particular, outness to family characterized LGBTQ young people’s experiences on Facebook.

Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

AbstractTo simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

Language, demographics, emotions, and the structure of online social networks

Social networks affect individuals’ economic opportunities and well-being. However, few of the factors thought to shape networks—culture, language, education, and income—were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.

An Access Control Model for Online Social Networks Using User-to-User Relationships

Users and resources in online social networks (OSNs) are interconnected via various types of relationships. In particular, user-to-user relationships form the basis of the OSN structure, and play a significant role in specifying and enforcing access control. Individual users and the OSN provider should be enabled to specify which access can be granted in terms of existing relationships. In this paper, we propose a novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification. Access control policies on users and resources are composed in terms of requested action, multiple relationship types, the starting point of the evaluation, and the number of hops on the path. We present two path checking algorithms to determine whether the required relationship path between users for a given access request exists. We validate the feasibility of our approach by implementing a prototype system and evaluating the performance of these two algorithms.

Machine Learning Techniques for Filtering of Unwanted Messages

Online Social Networking in these days is most powerful way to share the Information, thought, event and many more. In the Definition of technology we usually follow the Industry of Information Technology where, we describe the high-level contributions of this paper and discuss potential future research directions. Despite the massive popularity of online social networks, surprisingly little is known about how people are using them to connect and share content. To better understand the structure of online social networks, in this paper we conducted a large-scale measurement study that collected data on the social networks of four popular sites, covering over 12 million users and 400 million links, in order to remove the unwanted messages. Machine Learning Text Categorization is also used to categorize the short text messages.

Dynamical Behavior of Rumor in Online Social Networks

As a powerful tool for phase transition analysis, the mean-field theory of rumor spreading brings us a new method for research in Online Social Networks (OSN). Based on the theory of complex networks, we simulate the complex process of rumor spreading in OSN. Using numerical simulations with focus on the information spreading threshold as well as critical behaviors, we study the Susceptible-Infected-RecoveredSusceptible (SIRS) information dynamics model in OSN with asymmetric propagation. Basic principle and structure of OSN were analyzed. We present a rumor spreading model, where the influence of the neighbor trees is treated in a more realistic way and the definition of a neighborhood can be tuned by an additional parameter. Based on the mean-field theory approach, a contact network model with scale-free property is built. It is demonstrated that the asymmetry of propagation plays important role: we could redistribute the asymmetry to balance the degree heterogeneity of the network and then to restore the information threshold to a finite value. The relationship of spreading probability, network size, infected fraction and infected ratio are showed respectively in the paper. Our model exhibits a surprisingly sharp phase transition which can be shifted by a redefinition of the neighborhood. The result shows that the SIRS model built in this paper is valid and the simulation of the information propagation is feasible.

Bootstrap percolation on spatial networks.

Bootstrap percolation is a general representation of some networked activation process, which has found applications in explaining many important social phenomena, such as the propagation of information. Inspired by some recent findings on spatial structure of online social networks, here we study bootstrap percolation on undirected spatial networks, with the probability density function of long-range links’ lengths being a power law with tunable exponent. Setting the size of the giant active component as the order parameter, we find a parameter-dependent critical value for the power-law exponent, above which there is a double phase transition, mixed of a second-order phase transition and a hybrid phase transition with two varying critical points, otherwise there is only a second-order phase transition. We further find a parameter-independent critical value around −1, about which the two critical points for the double phase transition are almost constant. To our surprise, this critical value −1 is just equal or very close to the values of many real online social networks, including LiveJournal, HP Labs email network, Belgian mobile phone network, etc. This work helps us in better understanding the self-organization of spatial structure of online social networks, in terms of the effective function for information spreading.