Search

Covert channel is not only an important tool for concealment communication, but also an important means for hackers to transmit the stolen information. Since micro-blog became one of the most popular Internet applications, this paper investigated a covert channel based on micro-blog posting time. The poster mapped secret information into the micro-blog posting time, and recovered the original information from the posting time. It proposed four encoding methods based on three typical characteristics of micro-blog posting time. Analyses show that the encoding method which combines the time interval and the number of micro-blog achieves best performance. [ABSTRACT FROM AUTHOR]

Abstract: This study mainly focuses on the methodology of weighted graph clustering with the purpose of community detection for large scale networks such as the users’ relationship on Internet social networks. Most of the networks in the real world are weighted networks, so we proposed a graph clustering algorithm based on the concept of density and attractiveness for weighted networks, including node weight and edge weight. With deep analysis on the Sina micro-blog user network and Renren social network, we defined the user's core degree as node weight and users’ attractiveness as edge weight, experiments of community detection were done with the algorithm, the results verify the effectiveness and reliability of the algorithm. The algorithm is designed to make some breakthrough on the time complexity of Internet community detection algorithm, because the research is for large social networks. And the another advantage is that the method does not require to specify the number of clusters. [Copyright &y& Elsevier]

Aiming at the problem that micro-blog had large amount of information, the coalescing hierarchical clustering algorithm was not suitable and Single-Pass clustering algorithm results was not accurate, a new algorithm SP&HC integrating hierarchical clustering algorithm and Single-Pass clustering algorithm were put forward. It used Single-Pass clustering algorithm to make the large number of micro-blog text become into the simple clustering, in order to collect some small amplitude and high cohesive theme topic. This greatly streamlined the content and quantity of the topic, until making the theme topic hierarchical clustering algorithm to achieve the requirements; then it used hierarchical clustering algorithm to carry out a similar topic clustering, until that conditions met defaults. The simulation experiment results showed that the performance on recall and accuracy of the algorithm was better than the first two algorithms. [ABSTRACT FROM AUTHOR]

Social network analysis has been used to study complex networks by analysing their static structure and the dynamic changes. Although one of the newer forms of social media, micro-blogs have quickly become one of the most popular communication platforms. This popularity accounts for, in part, an increase in the scientific interest in micro-blogs and their users. In this paper, we chose as our test bed diabetes-related posts from the Chinese micro-blog Sina Weibo. We calculated the degree, average shortest path, betweenness and clustering coefficient of the Sian Weibo network to analyse its static structure. We demonstrate the characteristic results of average degree, diameter and clustering coefficient of diabetes micro-blog static structure. More importantly, we introduce a general model for micro-blog with directed network data, Exponential-family Random Graph Models (ERGMs). Meanwhile, we illustrate the utility for estimating, analysing and simulating micro-blog network. We also provide a goodness-of-fit approach to capture and reproduce the structure of the fitted micro-blog network. Parameter estimation of the model, similarity results of simulated networks and observed networks, and goodness of fit analysis for the micro-blog network all illustrate that ERGMs are excellent methods for deeply capturing complex network structures. [ABSTRACT FROM PUBLISHER]

Sina micro-blog (Weibo) is the first micro-blogging service in China and is growing fast in recent two years. This paper first studies the characteristics of Sina online social network and then focuses on the problem of indentifying influential users and automatic micro-blog classification. In a dataset prepared for this study, we find an approximate power-law follower distribution and a non-power-law friend distribution, a log correlation between follower number and tweet number, etc. In order to find the most popular users, we propose our algorithm called XinRank and compare it with the other two algorithms. The result shows that XinRank is different and it offers a new perspective for people to find influential users. In addition, our algorithm is dynamic and stability, which is special and better than the other two algorithms. We attempt to automatically classify a single Chinese micro-blog post into a set of high-level categories using a naive Bayes classifier. Our research indicates that even though an average micro-blogging post in Chinese is only 28 words in length, they can be categorized into one of eight categories with an average performance up to 84.2 %, using our proposed process. We try to address the automatic user interest discovery problem at the end of this paper. And finally, we combine XinRank and our micro-blog classifier to propose an interest-based influence ranking model. [ABSTRACT FROM AUTHOR]

Micro-blog has become one of the main Internet platforms for information acquireing and sharing. Prediction on information sharing is the basis for controlling and supervising information. Attributes of micro-blog users and information contain data of users' preferences, physiological characteristics, content type. etc. Based on these data can predict information sharing. This paper analyzed modes of micro-blog information dissemination, theory and methods for predicting information sharing, based on PA algorithm, it proposed information sharing prediction model. Through Sina micro-blog data verified the model, it shows that the model has high prediction accuracy of information sharing. [ABSTRACT FROM AUTHOR]

Combined with the important role of the time factor in the detection of hot topics, this paper studied how to effectively get the latest and the most valuable topic issues in the micro-blog based on the theory of user interest and the behavior between users, and proposed a hot topic detection algorithm (TimePageRank, which modified the PageRank algorithm) with a time argument. First, the algorithm extracted topics which were interesting to users by using the voting mechanism and recorded the generation time of the topic. Then, it calculated the weight of each topic. Finally, this paper used the proposed algorithm to rank these topics to detect hot topics in the micro-blog. The experimental results over real data set illustrate the effectiveness and efficiency provided by the algorithm. [ABSTRACT FROM AUTHOR]

There have been many domain-specific keyword extraction researches, but micro-blog- oriented keyword extraction is just beginning. This paper researches into the keyword extraction from Chinese micro-blog. Taking the characteristics of micro-blog into account, such as short, topic divergence, etc., we propose a Chinese micro-blog keyword extraction method based on the combination of multi features. Firstly create the graph model based on the co-occurrence between words, get a kind of weight based on the created graph model. The weight based on the graph model is sometimes same. In order to solve this problem, this method secondly proposes to create the semantic space based on the topic detection method, and get the statistical weight based on the semantic space. Finally, we take the location of words into account during the extraction, which is proved to be a very effective feature. Experimental results show that the proposed keyword extraction method is very successful. [ABSTRACT FROM AUTHOR]

Based on the analysis of the factors that affect retweet behavior, this paper proposed a predict SVM algorithm with five features: user influence, user activity, interest similarity, the importance of micro-blog content and users closeness. Furthermore, it proposed the predict algorithm of retweet scale on the basis of SVM, also, gave a method to evaluate the predict accuracy. The experiment with Sina micro-blog data shows a good result that the predict accuracy is up to 86.63%. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore consumers' views on enterprises conducting relationship marketing through micro-blogging. The basis of this study is the base on commitment-trust theory with the influences of consumers' satisfaction with the micro-blog page of enterprises, corporate images, commitment, trust, and sense of community on consumers' behavioral intention. The result of this study can be used as principles for enterprises to manage their relationship marketing through micro-blogging and construct their micro-blog page. The result of this study shows that consumers' satisfaction has positive influences on commitment, trust, sense of community, and behavioral intentions. The influences of corporate images on commitment and trust are supported, but the influences on behavioral intentions must go through the mediators, commitment and trust. The influences of trust on commitment and sense of community exist, but the influences on behavioral intentions must go through the mediators, commitment and sense of community. Lastly, the influences of commitment and sense of community on behavioral intentions exist. [ABSTRACT FROM AUTHOR]

In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today's social networks, it is necessary to accurately predict the scope and depth of private information diffusion. By doing so, it would be feasible to prevent and control the improper spread and diffusion of private information. In this paper, we propose an intelligent prediction method for private information diffusion in social networks based on comprehensive data analysis. We choose Sina Weibo, one of the most prominent social networks in China, to study. Firstly, a prediction model of message forwarding behavior is established by analyzing the characteristic factors that influence the forwarding behavior of the micro-blog users. Then the influence of users is calculated based on the interaction time and topological structure of users relationship, and the diffusion critical paths are identified. Finally, through the user forwarding probability transmission, we determine the micro-blog diffusion cut-off conditions. The simulation results on Sina Weibo data set show that the prediction accuracy is 86.9%, which indicates that our method is efficient to predict the message diffusion in real-world social networks. [ABSTRACT FROM AUTHOR]

How to find a user's interest from similar users a fundamental research problems in socialized recommender systems. Despite significant advances, there exists diversity loss for the majority of recommender systems. With this paper, for expanding the user's interest, we overcome this challenge by using representative and diverse similar users from followees. First, we model a personal user profile vector via word2vec and term frequency mechanisms. According to user profiles and their follow relationships, we compute content interaction similarity and follow interaction similarity. Second, by combining two kinds of interaction similarity, we calculate the social similarity and discover a diverse group with coverage and dissimilarity. The users in a diverse group can distinguish each other and cover the whole followees, which can model a group user profile (GUP). Then, by tracking the changes of followee set, we heuristically adjust the number of diverse group users and construct an adaptive GUP. Finally, we conduct experiments on Sina Weibo datasets for recommendation, and the experimental results demonstrate that the proposed GUP outperforms conventional approaches for diverse recommendation. [ABSTRACT FROM AUTHOR]

The public’s acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public’s attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the “follow-be followed” and “forward-dialogue” behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public’s attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology. [ABSTRACT FROM AUTHOR]

Text emotion analysis has long been a hot topic. With the development of social network, text emotion analysis on micro-blog posts becomes a new trend in recent years. However, most researchers classify posts into coarse-grained emotion classes, which cannot depict the emotions accurately. Besides, flat classification is mostly adopted, which brings difficulty for classifiers when given a large dataset. In this paper, by data preprocessing, feature extraction and feature selection, we classify Chinese micro-blog posts into fine-grained emotion classes, employing hierarchical classification to improve the performance of classifiers. Moreover, based on the regression values in classification procedure, we propose an algorithm to detect the principal emotions in posts and calculate their ratios. [ABSTRACT FROM AUTHOR]