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In view of the fractal evolution phenomenon in Internet information dissemination, this paper constructs a generalized fractal network by analyzing the characteristics of user dissemination behavior and according to the dissemination trend of actual information. On this basis, it reveals the fractal evolution mechanism in Internet information dissemination and its dissemination dynamic characteristics and rules. First, based on node similarity, a fractal propagation model of Internet information is constructed. Second, based on the scale-free network and according to the user characteristics, the information dissemination process is simulated and a generalized fractal network is evolved. Then, the key factors influencing the formation of fractal networks, the characteristics, and the rules of propagation dynamics are analyzed. Finally, the rationality and validity of the model are verified by crawling the real user forwarded data in the micro-blog. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a new human dynamics model to explain the process of verified users' (VUs) posting on Sina micro-blog. Design/methodology/approach: A common human dynamics research method with three steps is applied. Firstly, a large-scale behavioral dataset is collected involving 495 VUs and five topics on Sina micro-blog. Second, five important indicators that reveal the characteristics of posting behavior are analyzed. Then, a quantitative model is constructed to describe the process of posting behavior, and its validity is verified by simulations. Findings: Three important characteristics of VUs' micro-blog posting behavior are observed: fat-tailed distribution, fluctuation and periodicity. These characteristics do not fit the assumption of interest-driven models proposed by previous literature. An optimized task-driven model is introduced to describe this complex phenomenon mathematically. The model is verified on empirical data, confirming that task-driven models can be optimized to explore information behavior on social media. Originality/value: Being different from previous studies that mainly describe common users' posting behavior on social media by applying interest-driven models, this paper customizes an optimized task-driven model for VUs, who mainly treat social media as a platform for work and play a crucial role in information creation on social media. [ABSTRACT FROM AUTHOR]

This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed. • Predictive measurement tool to examine anxiety in tweets using a machine learning approach. • Machine learning approach depicts perceived user state-anxiety fluctuations and trait anxiety. • Perceived anxiety relates negatively to social engagement and popularity. • Implications include automatically assessing workers' wellbeing to reduce anxiety. [ABSTRACT FROM AUTHOR]

As the main carrier and platform of spreading network public opinion, micro-blog makes information disseminate more quickly and the influence of public opinion increased. Therefore, accurate analysis and prediction of micro-blog emotion are of great significance for predicting and controlling public opinion. In this paper, we propose the emotional component analysis and public opinion forecast on Chinese micro-blog posts based on maximum entropy model, which uses fine-grained to classify emotion of Chinese micro-blog. Firstly, we preprocess the Chinese micro-blog to filter the noise data. Moreover, the document frequency method and information gain principle are combined to extract features. Secondly, the maximum entropy model is employed to train classifier, and the selective integrated classifiers are used to analyze emotion. On this basis, the principle of the minority subordinate to the majority is used to predict public opinion. In addition, the experimental results have shown the accuracy of the proposed algorithm is 0.88, and the comparison of the four indicators of accuracy, recall, F-Measure and convergence error verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Most community discovery methods determine the best community according to network topology and edge density, however, they have very high computational complexity, and are very sensi¬tive to the form and type of the network. To solve t hese problems, we propose an interactive optimiza-tion algorithm based on dynamic nodes adaptive incremental model for micro-blog communities. By opti¬mizing the interaction among members in each community, the algorithm efficiently searches the candi¬dates of the optimal community without traversing all the nodes by using the greedy algorithm. The model allows rapid and accurate measurement of interaction difference within community and across com¬munities. Finally, simulations on the data grabbed from benchmark networks and Sohu micro-blog plat¬form show that the proposed algorithm outperforms other algorithms in recall rate, accuracy, computa¬tion time, and network coverage rate. [ABSTRACT FROM AUTHOR]

Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users. [ABSTRACT FROM PUBLISHER]

Purpose The purpose of this paper is to identify the key determinants of the information value of enterprises’ micro-blogs, drawing on the consumer’s perspective. This paper examines the effects of such information value on consumers’ purchase intention through the building of online brand equity. The paper also investigates how perceived usefulness intervenes in the relationship between micro-blog information value and consumer purchase intention.Design/methodology/approach The proposed research model is examined empirically using a survey of 315 users of the Sina micro-blog. The statistical analysis applies a method based on variance using partial least squares.Findings The results show that online retailers’ micro-blog posts, which are characterized by informativeness and persuasiveness as perceived by customers, positively impact online retailers’ brand equity, which in turn influences consumer purchase intention. In addition, perceived information usefulness is found to be an important contextual factor that moderates the relationship between perceived persuasiveness and online retailer brand equity.Originality/value This work adds significance to the literature by extending the research on micro-blog marketing and also provides practical recommendations for online retailers on the operation of micro-blogs. [ABSTRACT FROM AUTHOR]

In modern society, the micro-blog is an important social network. Repost is one of the most important features of micro-blog. However, the previous studies should have paid close attention to the motivation of repost and the interest-repost. Aiming at the feature of repost, the paper proposed an AT-IR model which was based on the original AT model tapping the interest-reposting to find the interest of micro-blog users. Besides , by means of crawling the micro-blog experimental data sets , this model is better than the AT model in the aspect of extraction accuracy. In addition , this method has shown the expansibility in the aspect of other subject model. [ABSTRACT FROM AUTHOR]

Persuasion aims at affecting the audience’s attitude and behaviour through a series of messages containing persuasion strategies. In the context of misinformation spread, identifying the persuasion strategies is important in order to warn people to be aware of the analogous persuasion attempts in the future. In this work, we address the prediction of persuasion strategies in micro-blogging posts through a multi-label classification approach based on a variety of lexical and semantic features. We conduct our experiments using a set of well-known multi-label classification algorithms, including multi-label decision tree, multi-label k-nearest neighbours, multi-label random forest, binary relevance and classifier chains. The results show that the model incorporating classifier chains and XGBoost algorithm achieves the best subset accuracy of 0.779 and the highest macro F1-score of 0.847. In addition, we evaluated and compared the features’ importance for different persuasion strategies and analysed the major errors of miss-out prediction. The findings of this article provide a benchmark for the multi-label classification of persuasion strategies in micro-blogging posts and lead to a better understanding of different persuasion attempts contained in social media misinformation. [ABSTRACT FROM AUTHOR]

There are a large number of false information and rumors in micro-blog. The wide-spread of rumors have seriously affected the social stability and damaged individual and national interests. In order to detect the micro-blog rumors effectively, this paper proposed a method based on deep features and ensemble classifier. Firstly, it extracted features from the sentiment orientation, propagation and user’s historical information of micro-blog. Then it trained the classifier by using these deep features. Finally, it detected micro-blog rumors based on the ensemble classifier. The experimental results show that the proposed method can effectively improve the classification performance. [ABSTRACT FROM AUTHOR]

Through a combining emotion thesaurus and semantic features of micro-blog, using space vector model to represent microblogging text, this paper proposed a method of microblogging text orientation analysis based on pattern matching and machine learning. For the text after participle, it firstly extracted the sentiment key words, and then matched the key words and analyze several models to extract text phrases of sentiment evaluation, emotional phrases, micro-blog emoticons and other emotional features characteristic constitute micro-blog sentiment characteristic sequence. Finally, it made use of SVM machine learning algorithms to get sentiment tendencies of micro-blog text. Evaluation results are compared with other paper, the experimental results show that this method is effective. [ABSTRACT FROM AUTHOR]

Problem-based learning (PBL) is regarded as an effective learning strategy to enhance cognition not only in traditional learning but also in e-learning. In e-learning settings, blogs and micro-blogs can plausibly provide a platform to conduct PBL. Recently, most studies on blogs and micro-blogs have respectively probed satisfaction and learning effectiveness. However, little effort has been made to explore their effectiveness on learning. Therefore, this study explored an application of a PBL approach on a micro-blog and blog by seventh-grade students taking an information ethics course; in particular, the effect of a micro-blog and blog on student learning with different learning achievement levels. Moreover, results showed that the micro-blog had more effect on PBL for low achievement students compared to the blog. This study suggests that using a micro-blog for PBL is more appropriate for the sake of maximizing learning effectiveness, if generally considering students with different learning achievement levels. [ABSTRACT FROM AUTHOR]

To promote public understanding of Europe and Sino-European relations, on 24 August 2012, the Ministry of Foreign Affairs of the People's Republic of China opened an official micro-blog through a Sina Weibo account named ‘@Zhongou Xinshi’ (means ‘Sino–Europe messenger’ in English). This article uses content analysis methods to examine the posts in this micro-blog so as to find out what is European Union's image in the eyes of Chinese official social media. The main findings of this study revealed that the image of Europe shaped in this Weibo is neutral, i.e., no anti-European sentiments are being expressed. The authors suggest that Chinese authorities need to make efforts to promote dialogue between China and Europe to accurately and effectively communicate via social media. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to explore how to use social media in e-government to strengthen interactivity between government and the general public. Design/methodology/approach – Categorizing the determinants to interactivity covering depth and breadth into two aspects that are the structural features and the content features, this study employs general linear model and ANOVA method to analyse 14,910 posts belonged to the top list of the 96 most popular government accounts of Sina, one of the largest social media platforms in China. Findings – The main findings of the research are that both variables of the ratio of multimedia elements, and the ratio of external links have positive effects on the breadth of interactivity, while the ratio of multimedia features, and the ratio of originality have significant effects on the depth of interactivity. Originality/value – The contributions are as follows. First, the authors analyse the properties and the topics of government posts to draw a rich picture of how local governments use the micro-blog as a communications channel to interact with the public. Second, the authors conceptualize the government online interactivity in terms of the breadth and depth. Third, the authors identify factors that will enhance the interactivity from two aspects: structural features and content features. Lastly, the authors offer suggestions to local governments on how to strengthen the e-government interactivity in social media. [ABSTRACT FROM AUTHOR]

For the disadvantages that the existing prediction models without considering the neighbors' influence and the difference between long term profiles and short term profiles, this paper proposed a prediction model of microblog users' profiles, which was based on RBF neural network to improve the prediction precision and enhance the recommendation efficiency of products and service. The model divided the profiles into long term and short term profiles, and users' influence into themselves and neighbors'. Based on this, this model adopted the RBF regularization network which had predominant learning ability and closeness to predict users' profiles.In the experiment on Tencent Weibo, about 4.31%, 14.53% average prediction offset and 0.31, 48.12 average variance of prediction offset for long term profiles and short term profiles respectively are achieved, and the result shows that this method is superior to others in prediction precision and stability. [ABSTRACT FROM AUTHOR]

Emotion analysis and emotion cause extraction are key research tasks in natural language processing and public opinion mining. This paper presents a rule-based approach to emotion cause component detection for Chinese micro-blogs. Our research has important scientific values on social network knowledge discovery and data mining. It also has a great potential in analyzing the psychological processes of consumers. Firstly, this paper proposes a rule-based system underlying the conditions that trigger emotions based on an emotional model. Secondly, this paper extracts the corresponding cause events in fine-grained emotions from the results of events, actions of agents and aspects of objects. Meanwhile, it is reasonable to get the proportions of different cause components under different emotions by constructing the emotional lexicon and identifying different linguistic features, and the proposed approach is based on Bayesian probability. Finally, this paper presents the experiments on an emotion corpus of Chinese micro-blogs. The experimental results validate the feasibility of the approach. The existing problems and the further works are also present at the end. [ABSTRACT FROM AUTHOR]

With the fast development of micro-blogs, there is an unprecedented abundance of information shared online. Meanwhile, information overload appears. Due to the continually increasing nonstructural contents and the complexity that characterizes the structure of social networks, the deployment of online customized recommendation systems is very challenging. In this article a collaborative filtering recommendation model integrating information diffusion has been designed to solve the problem of data sparsity and cold-start problem in the context of micro-blog websites. Firstly, the proposed framework constructs a user-keyword interest model by using natural language processing technology to handle nonstructural contents. Keywords extracted thereafter act as recommended items. Secondly, the research uses a first-order Markov random work model to simulate the processing of users' preference' diffusion in social networks' The user-keyword preference matrix is then conducted. Lastly, the experiments are conducted with real world dataset from Weibo. The effectiveness of the proposed recommendation model is evaluated by using three assessment criteria (mean absolute error, precision and recall). The results show that the collaborative filtering recommendation model integrating information diffusion performs better than benchmark models which do not consider social networks. [ABSTRACT FROM AUTHOR]

In recent years, the text sentiment analysis has always been a hot iisue in the field of natural language processing. As a short text, micro-blog is featured of refined and concise, rich in views, tendencies and attitudes. Thus, the identification of emotional tendencies has important practical significance. This paper proposed a method of sentiment analysis based on SVM and CRF, used various features including word, speech, emotional word, negative word, adverb of degree and special symbols. They designed diiferent combinations of features to make the effect optimal through multifile sets of experiments. The accuracy of SVM reached 88.72% using the combination of speech, sentiment word and negative word, while CRF attained 90.44% selecting the combination of sentiment word, negative word, adverb of degree and special symbols. [ABSTRACT FROM AUTHOR]

Social networks provide people a platform for expressing their own emotion, viewpoints, interest and suggestion. The users' texts, behaviors and social circles bring new challenges for data mining. In this article, we propose a new method through which we can classify and predict micro-blog users' MBTI personality values by utilizing their shared micro-blog information. Firstly, based on the analysis of micro-blog users' information, texts and non-text features, which can indicate users' psycho-linguistic and behavioral characteristics, are extracted. And then three classification methods--AdaBoost decision tree, support vector machine and Bayesian logistic regression, are adopted to classify the personalities of micro-blog users. Extensive experimental results indicate that the prediction accuracy are between 75%and 90%for the four different personality dimensions of MBTI. [ABSTRACT FROM AUTHOR]

In recent years, we media, i.e., micro-blogs, have drawn increasing attention to their roles by exposing food safety scandals in China. In this paper, a typical case of a scandal concerning “set-style yogurt and jelly event” reported by a micro-blog was studied and analyzed in terms of its dissemination mode and the mechanism of how a micro-blog from “opinion leaders” affected public attention. Related sales data of set-style yogurt and jelly products were obtained after the event from a supermarket corporation in Beijing and used to analyze trends affected by the scandal. The results indicate that in the new media age, we media has greatly increased dissemination of the voices of “opinion leaders” and triggered large-scale communication of food safety messages to the public. In this serious information asymmetry situation between public and food manufacturer, food-safety scandals revealed by we media could easily be noticed by consumers and further affect their judgments of expected utility and their purchasing behavior. Sales volume decreases and negative influence on brand reputation might contribute to correcting food manufacturers’ behavior. We media could be an effective tool to improve the food-safety status of the market. [ABSTRACT FROM AUTHOR]

Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter™API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]