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.

The Effect of Perceived Anonymity on Online Transgressions:The Moderating Role of Moral Excuses

With the development of science and technology,the use of the Internet is becoming more and more widespread.However,with the popularity of the Internet,some problems have gradually surfaced.The anonymity of Internet use has become a breeding ground for many acts that are contrary to public decency,and this study is conducted against this background.This study explored the impact of perceived anonymity on online transgressions and investigated the moderating effect of moral excuses.A total of 414 subjects,210 males and 204 females,participated in this experimental survey.The SPSS data analysis concluded that perceived anonymity played a significant positive predictive role on online deviance(p<0.01),and the moderating role of moral excuses was not significant.This study will be conducive to the better implementation of the action of clearing cyberspace and to the regulation of public behaviour in cyberspace.

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.

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.

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.

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.

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.

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.

Online Social Support and Use of SNSs Among College Students Relationship to Online and Offiine Social Skills

The subjects of this study are college students aged between 18 to 26 years old from different majors. Investigated the use intensity and addiction of SNSs among college students, with variables of use behaviors and online social support are relate to their online social skills and offline social skills. Seven point Likert scale, descriptive statistics, One-way ANOVA and logistic regression were used to determine the correlations of different use intensities and levels of addiction. The results explored whether college students＇ online social skills, offline social skills, and online social support are correlated with use intensity and addiction. Long-term or high-frequency use of SNSs does not lead to equivalent social relationships on SNSs, yet addiction to SNSs can reflect the positive support and communication ability of college students in online interpersonal relationships.

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.

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.

Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis

With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

Machine Learning Techniques for Detecting Phishing URL Attacks

Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens humans.The defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks.Cyber-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information.One of the online security challenges is the enormous number of daily transactions done via phishing sites.As Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management process.This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

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.

ComRank： Joint Weight Technique for the Identification of Influential Communities

Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people. However, there is little work to shed light on how to rank communities while considering their levels that are determined by the quality of their published contents. In this paper, we propose solution for measuring the influence of communities and ranking them by considering joint weight composed of internal and external influence of communities. To address this issue, we design a novel algorithm called Com Rank: a modification of Page Rank, which considers the joint weight in order to identify impact of each community and ranking them. We use real-world data trace in citation network and perform extensive experiments to evaluate our proposed algorithm. The comparative results depict significant improvements by our algorithm in community ranking due to the inclusion of proposed weighting feature.

Evaluating Group Formation in Virtual Communities

In this paper, we are interested in answering the following research question: "Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities?"In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community(called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.

Evolution-Based Performance Prediction of Star Cricketers

Cricket databases contain rich and useful information to examine and forecasting patterns and trends.This paper predicts Star Cricketers(SCs)from batting and bowling domains by employing supervised machine learning models.With this aim,each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers.Prediction is performed by applying Bayesianrule,function and decision-tree-based models.Experimental evaluations are performed to validate the applicability of the proposed approach.In particular,the impact of the individual features on the prediction of SCs are analyzed.Moreover,the category and model-wise feature evaluations are also conducted.A cross-validation mechanism is applied to validate the performance of our proposed approach which further confirms that the incorporated features are statistically significant.Finally,leading SCs are extracted based on their performance evolution scores and their standings are cross-checked with those provided by the International Cricket Council.

A Multi-Agent Based Model for User Interest Mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations,advertising,marketing promotions,and other tasks.Although great progress has been made in this area,previous studies have ignored the differences among users:the varied behaviors and habits that lead to unique user data characteristics.It is unreasonable to use a single strategy to mine interests from such varied user data.Therefore,this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data,microblogs and correlations.This method has the ability to select the appropriate strategy based on each user’s data characteristics.The experimental results show that the proposed method performs better than the baselines.

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.