CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

Effect of Online Social Networking on Emotional Status and Its Interaction with Offline Reality during the Early Stage of the COVID-19 Pandemic in China

Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.

Identification of Influential Users in Online Social Network: A Brief Overview

Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.

Modeling online social networks based on preferential linking

We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro=mechanisms of network growth and the macrostructures of online social networks.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

ED-SWE:Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People.In this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets.The proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams automatically.Moreover,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data.In order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely.Finally,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event.Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.

Towards understanding bogus traffic service in online social networks

Critical functionality and huge infuence of the hot trend/topic page(HTP)in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service(BTS).BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public opinions.To hijack HTP,the agents of BTS maintain an army of black-market accounts called bogus trafic accounts(BTAs)and control BTAs to generate a burst of fake trafic by massively retweeting the tweets containing the customer desired topic(hashtag).Although this service has been extensively exploited by malicious customers,little has been done to understand it.In this paper,we conduct a systematic measurement study of the BTS.We first investigate and collect 125 BTS agents from a variety of sources and set up a honey pot account to capture BTAs from these agents.We then build a BTA detector that detects 162218 BTAs from Weibo,the largest Chinese microblogging site,with a precision of 94.5%.We further use them as a bridge to uncover 296916 topics that might be involved in bogus trafic.Finally,we uncover the operating mechanism from the perspectives of the attack cycle and the attack entity.The highlights of our findings include the temporal attack patterns and intelligent evasion tactics of the BTAs.These findings bring BTS into the spotlight.Our work will help in understanding and ultimately eliminating this threat.

RecGuard: An efficient privacy preservation blockchain-based system for online social network users

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and e-commerce.However,it raises several questions about privacy,especially for users who prefer to remain anonymous,especially on online social networks(OSNs).Moreover,due to the commercialization of online users'data,some service providers sell users'data to third parties at the blind side of the users,which leads to trust issues between users and service providers.Such matters call for a system that gives online users much-needed control and autonomy of their data.With the advancement of blockchain technology,many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and hacks.To resolve these limitations,we propose RecGuard,a privacy preservation blockchain-based network system.We developed two smart contracts,RG-SH and RG-ST,to ensure the security and privacy of user data.The RG-SH manages user data,whereas the RGST stores data.A graph convolutional network(GCN)was integrated with the blockchain-based system to detect malicious nodes.Finally,we implemented our framework prototype on a locally simulated network.The analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network.

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

In social data analytics,Virtual Community(VC)detection is a primary challenge in discovering user relationships and enhancing social recommenda-tions.VC formation is used for personal interaction between communities.But the usual methods didn’t find the Suspicious Behaviour(SB)needed to make a VC.The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking(GJSBS-RDNNCR)Model addresses these issues.The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks(SN).In the GJSBS-RDNNCR model,the SN is given as an input at the input layer.After that,the User’s Behaviors(UB)are extracted in the first Hidden Layer(HL),and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the SB.In the third HL,the similarity values are examined,and SB tendency is classified using the Activation Function(AF)in the Output Layer(OL).Finally,the ranking process is performed with classified users in SN and their SB.Results analysis is performed with metrics such as Classification Accuracy(CA),Time Complexity(TC),and False Positive Rate(FPR).The experimental setup consid-ers 250 tweet users from the dataset to identify the SBs of users.

Encrypted data sharing with multi-owner based on digital rights management in online social networks

The online social networks（OSNs） offer attractive means for social interactions and data sharing, as well as raise a number of security and privacy issues. Although current solutions propose to encrypt data before sharing, the access control of encrypted data has become a challenging task. Moreover, multiple owners may enforce different access policy to the same data because of their different privacy concerns. A digital rights management（DRM） scheme is proposed for encrypted data in OSNs. In order to protect users＇ sensitive data, the scheme allows users outsource encrypted data to the OSNs service provider for sharing and customize the access policy of their data based on ciphertext-policy attribute-based encryption. Furthermore, the scheme presents a multiparty access control model based on identity-based broadcast encryption and ciphertext-policy attribute-based proxy re-encryption, which enables multiple owners, such as tagged users who appear in a single data, customize the access policy collaboratively, and also allows the disseminators update the access policy if their attributes satisfy the existing access policy. Security analysis and comparison indicate that the proposed scheme is secure and efficient.

A Differentially Private Auction Mechanism in Online Social Networks

The growing popularity of users in online social network gives a big opportunity for online auction.The famous Information Diffusion Mechanism(IDM)is an excellent method even meet the incentive compatibility and individual rationality.Although the existing auction in online social network has considered the buyers’information which is not known by the seller,current mechanism still can not preserve the privacy information of users in online social network.In this paper,we propose a novel mechanism based on the IDM and differential privacy.Our mechanism can successfully process the auction and at the same time preserve clients’price information from neighbours.We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process.We also formulate this mechanism on the real network and the random network,scale-free network to show the feasibility and effectiveness of the proposed mechanism.The evaluation shows that the result of our methods only depend on the noise added to the agents.It is independent from the agents’original price.

Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social Networks

The COVID-19 pandemic has hit the world hard.The reaction to the pandemic related issues has been pouring into social platforms,such as Twitter.Many public officials and governments use Twitter to make policy announcements.People keep close track of the related information and express their concerns about the policies on Twitter.It is beneficial yet challenging to derive important information or knowledge out of such Twitter data.In this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment analysis.We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data.Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities.The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

Discrete Opinion Dynamics on Online Social Networks

This paper focuses on the dynamics of binary opinions {＋1,-1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors＇ strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-amrmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors＇ strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.

Online social network model with renewal and accelerated growth

Based on observation of the growing mechanism in Twitter-like online social networks, an online social network （OSN） evolution model was proposed, a renewal mechanism for the old nodes and an accelerated growth mechanism was introduced for the new nodes, comparing with the native copying model. Topological characteristics of the generated networks, such as degree distribution, average shortest-path length and clustering coefficient, are analyzed and numerized. These properties are validated with some crawled datasets of real online social networks.

Analysis of the Characteristics and Model of Online Social Network Information Dissemination

Online social network is increasingly showing a significant impact and role in many areas of social life. In the study of online social network related issues have become the consensus of the academic and industrial communities and the urgent need for. This paper mainly studies the problem of information dissemination in social network, the mode of communication, behavior, propagation paths and propagation characteristics are studied, and take the Tencent micro-blog as an example, based on the analysis of many examples, several main models and characteristics of information dissemination in social network platform.

Modeling Reading and Replying Activities in a BBS Social Network

This paper is devoted to analyze and model user reading and replying activities in a bulletin board system （BBS） social network. By analyzing the data set from a famous Chinese BBS social network, we show how some user activities distribute, and reveal several important features that might characterize user dynamics. We propose a method to model user activities in the BBS social network. The model could reproduce power-law and non-power-law distributions of user activities at the same time. Our results show that user reading and replying activities could be simulated through simple agent-based models. Specifically, manners of how the BBS server interacts with Internet users in the Web 2.0 application, how users organize their reading lists, and how user behavioral trait distributes are the important factors in the formation of activity patterns.

UAV Online Path Planning Algorithm in a Low Altitude Dangerous Environment

UAV online path-planning in a low altitude dangerous environment with dense obstacles, static threats (STs) and dynamic threats (DTs), is a complicated, dynamic, uncertain and real-time problem. We propose a novel method to solve the problem to get a feasible and safe path. Firstly STs are modeled based on intuitionistic fuzzy set (IFS) to express the uncertainties in STs. The methods for ST assessment and synthesizing are presented. A reachability set (RS) estimator of DT is developed based on rapidly-exploring random tree (RRT) to predict the threat of DT. Secondly a subgoal selector is proposed and integrated into the planning system to decrease the cost of planning, accelerate the path searching and reduce threats on a path. Receding horizon (RH) is introduced to solve the online path planning problem in a dynamic and partially unknown environment. A local path planner is constructed by improving dynamic domain rapidly-exploring random tree (DDRRT) to deal with complex obstacles. RRT∗ is embedded into the planner to optimize paths. The results of Monte Carlo simulation comparing the traditional methods prove that our algorithm behaves well on online path planning with high successful penetration probability. © 2014 Chinese Association of Automation.