Microblog Users Research Articles

Implicit Community Discovery Based on Microblog Theme Homogeneity

With the rapid development of various social platforms, social networks have accumulated more and more data. These huge and complex data contain valuable information in many fields. It is of far-reaching significance to conduct statistical analysis of these data and dig out valuable information and apply it in real life. Based on the homogeneity of social networks, our paper proposes a framework of discovery algorithms based on the homogeneity of microblogs to realize the theme community discovery of microblog social networks. Firstly, the data set is preprocessed, the invalid data is removed and classified by microblog users. Secondly, the LDA model is used to extract the topic of microblog user blogs to describe the characteristics of microblog users. Thirdly, two users are calculated based on the theme interest. The homogeneity measure between them is used to represent the relationship of social networks. Finally, the community of the implicit social network is realized through unsupervised algorithms to build communities with the same interest as the theme. In order to verify the validity of our proposed algorithm framework, this paper uses data sets from microblog network to conduct experiments. The experimental results show that the algorithm effectively combines the homogeneity measure of the network and has achieved very good results in the implicit community discovery.

An empirical study of brand microblog users’ unfollowing motivations: The perspective of push-pull-mooring model

Brand microblogs (BMs) have been increasingly utilized by companies to facilitate communication and foster deeper relationships with their customers. In addition to attracting new followers, retaining existing followers is equally, if not more, important to the success of BM operators. Drawing upon the migration theory, this study develops a push-pull-mooring (PPM) model of BM unfollowing motivations to enhance our understanding of the significant antecedents that promote BM users’ unfollowing intention. The study empirically investigates the three categories of antecedents of the BM unfollowing intention: push (dissatisfaction with information quality, dissatisfaction with service quality, and person brand unfit), pull (alternative attractiveness), and mooring (perceived unfollowing costs) effects. The results suggest that the three groups of unfollowing motivations display varying degrees of influence on BM users’ unfollowing intention. Theoretical and managerial implications of the findings of this study are also discussed.

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Microblogs are open and real-time online social network platforms used by people to make posts about their moods, experiences, and interests. It will be very significant to gather microblog users who have similar interests and hobbies into the same community. In this paper, we propose novel approaches for detecting and evolving dynamic microblog communities. First, inspired by the universal gravitation law, we redefine the gravitation relationships among microblog users. Based on the structure of the microblog social network, we define the basic nodes and their gravity tendency and propose the microblog community detection algorithm. Second, we determine the community changes in the microblog social networks at times $t$ and $t+1$ and propose a microblog community evolution algorithm. Third, we define the mutual transformation probability between communities at times $t$ and $t+1$ and propose the microblog community evolution behavior algorithm. The experiment includes a comparison and evaluation of the microblog community detection, evolution, and behavior extraction algorithms and the optimal ranges of the parameters involved in these algorithms. The experimental results indicate that our proposed algorithms have good performance compared to other benchmarking methods.

Application of machine learning and cloud computing in social media behavior analysis

It is of great theoretical significance and practical value to analyze the characteristics of users and behaviors in social networks, to study the personalized recommendation algorithms of users, to explore the inherent laws of event development, and to predict the movement of information or opinions. This paper analyzes the Weibo behavior through machine learning and cloud computing technology. Moreover, this paper studies and analyzes traditional network algorithms, and proposes a microblog recommendation algorithm based on statistical features. At the same time, the research content of this paper focuses on microblog contents, user characteristics, user preferences, and influence levels. The algorithm has simple structure and strong computing performance and performs feature data mining through cloud computing big data method, which is suitable for online mining microblog behavior. In addition, the performance of the algorithm was analyzed by design comparison experiments. The research indicates that the research algorithm proposed in this paper has certain advantages, which can be applied to network behavior analysis mining, and can provide theoretical reference for subsequent related research.

Forwarding Behavior Prediction Based on Microblog User Features

In microblog networks, when a user posts a microblog, other users may forward the post, and then the forwarding process will bring about the rapid dissemination and diffusion of information. In this paper, we propose a comprehensive and novel approach to predict user forwarding behavior. Firstly, we build the feature sets that affect the microblog forwarding, such as interest topic,geographic location, user aggregation coefficient,neighborhood overlap and so on. These features are classified into four categories: user characteristics, microblog features, network structure features, and interactive behavior characteristics. Secondly, we establish a feature selection model based on Filtering and Wrapping for predicting the forwarding behavior of users. The model includes three aspects: (1)ANOVA(Analysis of variance): The value of each feature is analyzed by variance analysis. If the feature variance is small, the feature provides less information. (2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> test and point-two-column correlation analysis: They filter discrete and continuous features, respectively. (3)Wrapper analysis: In order to solve the strong correlations between the features, we use LVW(Las vagas wrapper) algorithm to analyze the above feature sets, and then obtain the optimal feature combination. Finally, we propose the forwarding prediction model based on AdaBoost(Adaptive boosting) algorithm. Experimental results demonstrate that the model has the highest precision and F1 score than Naive Bayes, Logistic Regression, Random Forest and SVM(Support vector machine), and the F1 score reached 0.885. Among different topics, our proposed AdaBoost prediction model has good recall and F1 scores for different topics. In addition, by using different feature sets for comparison experiments, it is found that the optimal features selected in this paper are very effective.

DUAPM: An Effective Dynamic Micro-Blogging User Activity Prediction Model Towards Cyber-Physical-Social Systems

Recent emergence of “microblogging” services has been driving cyber-physical social system (CPSS) as a hot topic in real-world applications. How to efficiently detect and recognise spam and fake accounts becomes an important task where it requires analysis of microblog user behavior and prediction of their activity. This article attempts to investigate this challenge by proposing a new strategy to effectively model microblogging user activity and dynamically predicting their activities for the CPSS applications. We first analysis and define a set of benchmarks for measuring microblogging user activeness in considering serval key dynamic attributes including change rate of microblogging numbers, user attentions, etc. Then, we build up a new dynamic microblogging user activity prediction model (DUAPM) based on three important characteristics: personal information, social relationship, and user interaction. Finally, an improved logical regression algorithm is proposed for training the model and predicting user activity. Under the evaluation of a sample dataset containing Sina Weibo 3621 users over 20 weeks, it shows that our model deliver average up to 3% higher prediction accuracy than other social media user activity prediction models using traditional logical regression and random forest algorithms. We also take out a CPSS case study of evaluating DUAPM models for analysis and prediction of Twitter users’ activity over 16 countries. The results show that our model effectively reflects the distribution and trends of Twitter users’ activity with different background and cultures.

Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data

As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.

Influence Calculation Model of Microblog User Based on Content, Emotion and Users

Influence Calculation Model of Microblog User Based on Content, Emotion and Users

A content-location-aware public welfare activity information push system based on microblog

Microblog has become a widely used social media tool, which can help users publish or obtain Public Welfare Activity (PWA) information. Due to the characteristics of large-scale and arbitrary in microblog use, a large amount of messy information is produced, which makes it difficult to discover interesting PWA information. Thus, developing a push system, which can seek a certain number of local potential users for a PWA to be Pushed (PWAP), would be a great help. However, it is challenging to discover users explicitly interested in PWAs and obtain a certain number of local potential users. In this paper, a Content-Location-Aware PWA information Push system based on microblog (CLAP) is proposed, which can seek a certain number of local potential users for a PWAP from massive microblog users, and push the corresponding information to them. Specifically, a topic model and keyword search based method is firstly utilized to discover users who are explicitly interested in the PWAP (i.e., seed users). Secondly, a distance metric learning based method is utilized to obtain a certain number of local potential users by measuring the similarities between seed users and local users. Experimental results demonstrate that the proposed system has a potential to generate superior results.

Correction to: Characterising and evaluating dynamic online communities from live microblogging user interactions

The original version of the article unfortunately contained an error in Acknowledgements section. Below is the corrected version.

English as a linguistic resource

With the intensification of globalization and advancement of digital communication, interactions online have gained increasing vitality in our life. Online communication now influences the spread of various languages (e.g., English) and semiotic signs (e.g., emoticons) to many countries, especially for those characterized by Kachru (1986) as belonging to the Expanding Circle (e.g., Japan, China — countries where English is usually practiced as a foreign language). Recent inquiries of digital discourse analysis have researched how English and its features are adopted in countries of the Expanding Circle (e.g., Crystal, 2008; Zhang, 2012). This paper continues to explore the linguistic landscape of English practices online, and specifically focuses on how micro-blogging users in mainland China adopt English as a resource for meaningful multilingual communication.

The multi-tag semantic correlation used for micro-blog user interest modeling

Tags play an important role in expressing the interests and attributes of microblog users, but the implicit semantic meaning of user tags is often ignored. We propose an improved micro-blog user interest modeling approach based on multi-tag semantic correlation via analyzing tag relationship and the limitations of the existing micro-blog user interest models. Firstly, the co-occurrence frequency of tag pair is calculated from the micro-blog user collection to obtain the intra-correlation between tag pair, the path is constructed based on the linking tags for each tag pair and the inter-correlation of tag pair is obtained via the shared entropy. Secondly, usertags are clustered and representative tags are obtained according to the similarity between tags, to construct the tags-representative tags matrix. Finally, we combine the above two correlations to acquire the semantic correlation matrix, based on which the users-tags matrix can be updated, thus the micro-blog user interest model based on multi-tag semantic correlation can be obtained. We evaluate our method through a series of experiments based on a dataset crawled from the open API and the results are analyzed. The results show that the interest expression model of microblog users proposed in this paper can better represent the interest characteristics of users.

Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model

Everything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonlinearly dependent. However, to the best of our knowledge, the nonlinear influence predication of social network has not been explored in the existing literature. This article proposes a partial autoregression single index model to combine network structure (linear) and static covariates (nonparametric) flexibly. Compared with previous work, our model has fewer limits and more applications. The profile least squares estimation is employed to infer this semiparametric model, and variables selection is performed via the smoothly clipped absolute deviation penalty (SCAD). Simulations are conducted to demonstrate finite sample behaviors.

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.

How mood affects the stock market: Empirical evidence from microblogs

Examining 22,504 tweets extracted from Sina Weibo, a microblog site, we identify two clusters of microblog users and study how they influence the stock market. Our research contributes the following significant findings to the existing literature. First, we discover that there exists an inverse U-shaped curve between stock return and the attention of both news media and investors. Second, we verify that news media attention plays a positive moderating effect in the relationship between investor attention and the stock return. Finally, we find that social interaction could positively moderate the effect of news media’s and investor’s sentiments on stock return.

Characterising and evaluating dynamic online communities from live microblogging user interactions

Microblogging social media focuses on fast open real-time communication using short messages between users and their followers. These platforms generate large amounts of content, and community finding techniques are a suitable alternative for organising it. However, there is no clear agreement in the literature for a definition of user community for the microblogging use case, leading to unreliable ground-truth data and evaluation. In this work, we differentiate between functional and structural definitions of communities for microblogging. A functional community groups its users by a common independent social function, e.g. fans of the same football team, while in a structural community the members exclusively depend on their connectivity in a network, e.g. modularity. We build and characterise eight types of functional communities to be used as user-labelled ground-truth and five types of user interactions networks from Twitter. We then evaluate—in static and dynamic scenarios—thirteen popular structural community definitions using five different Twitter datasets, exploring their goodness and robustness for detecting the functional ground-truth under different perturbation strategies. Our results show that definitions based on internal connectivity, e.g. Triangle Participation Ratio, Fraction Over Median Degree or Conductance work best for the Twitter use case and are very robust. On the other hand, other scores such as Modularity are limited and do not perform well due to the sparsity and noise of microblogging. Furthermore, using user activity as basis to separate communities into active hotspots further improves the performance of community detection in microblogging.

Examining the influential factors for continued social media use

PurposeDrawing upon the theory of planned behavior (TPB) and the self-regulation framework, the purpose of this paper is to investigate whether and how factors for social media continuance behaviors work differently between social networking sites and microblogging.Design/methodology/approachA survey method was used to collect two samples of 557 social networking sites users and 568 microblogging users. The proposed research model was tested with the structural equation modeling technique.FindingsThe empirical results demonstrate that the impacts of influencing factors on users’ continuance behaviors vary by types of social media services. Information sharing has a stronger impact on microblog users’ satisfaction than social network users while social interaction has a stronger impact on satisfaction for social network users than microblog users. In addition, interpersonal influence is more effective in shaping satisfaction for the social network users while media influence is more effective in shaping satisfaction for the microblog users.Originality/valueThis is one of the first studies that integrate TPB with Bagozzi’s self-regulation framework to understand the behavioral model of social networking and microblogging continuance. The findings show that the impacts of attitudinal beliefs regarding information sharing and social interaction on social media users’ satisfaction are different across social networking and microblogging contexts. Moreover, this study also reveals different effects of two specific subjective norms – interpersonal and media influence – on continued use of social networking and microblogging.

Understanding users’ continuous content contribution behaviours on microblogs: an integrated perspective of uses and gratification theory and social influence theory

ABSTRACTMicroblogs are revolutionising the way users produce, consume and distribute short content. The continuous content contributions of users are crucial for the sustainable development of microblogs. Knowledge regarding this user behaviour is scarce. Based on the uses and gratifications theory (U&G) and the social influence theory (SIT), this paper proposes an integrated research model with the aim of understanding the factors that affect users’ continuous content contribution behaviours (CCCB) on microblogs. The data were collected from 379 microblog users in China. The empirical results indicate that perceived gratification had a positive but surprisingly trivial effect on continuous content contribution behaviours. Social influence had a strong and significantly positive effect on users’ CCCB, it also had a positive moderating effect on the relationship between perceived gratification and CCCB. Users’ perceived gratification was positively affected by the antecedents of self-expression, anticipated extrinsic rewards and anticipated reciprocity but negatively affected by privacy concerns. The platform attributes of microblogs (i.e. accessibility and interactivity) also influenced users’ perceived gratification. This research contributes to social media research with regards to the theory of users’ CCCB.

Machine Learning Methods to Predict Social Media Disaster Rumor Refuters.

This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted microblog content of 3793 Sina Weibo users was collected. Natural language processing (NLP) was used for the sentiment and short text similarity analyses, and four machine learning techniques, i.e., logistic regression (LR), support vector machines (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were compared on different rumor refuting microblogs; after which a valid and robust distinguishing XGBoost model was trained and validated to predict who would retweet disaster-related rumor refuting microblogs. Compared with traditional prediction variables that only access user information, the similarity and sentiment analyses of the most recent user microblog contents were found to significantly improve prediction precision and robustness. The number of user microblogs also proved to be a valuable reference for all samples during the prediction process. This prediction methodology could be possibly more useful for WeChat or Facebook as these have relatively stable closed-loop communication channels, which means that rumors are more likely to be refuted by acquaintances. Therefore, the methodology is going to be further optimized and validated on WeChat-like channels in the future. The novel rumor refuting approach presented in this research harnessed NLP for the user microblog content analysis and then used the analysis results of NLP as additional prediction variables to identify the anti-rumor spreaders. Therefore, compared to previous studies, this study presents a new and effective decision support for rumor countermeasures.

Building a TIN-LDA Model for Mining Microblog Users’ Interest

A latent Dirichlet allocation (LDA) model is a common method for mining the interest of microblog users. But the LDA model does not reflect the hierarchical and dynamic trend of microblog users' interest. As a result, this paper combines with the timeliness and interactivity of microblog, to judge the hierarchical orientation and dynamic interest trend orientation of users' interest. And based on the dynamic interest hierarchical orientation, the three-layers interest network (TIN-LDA) model is constructed to mine the interest of microblog users. In addition, this model expands interest attributes. Interest attributes include contents, contents marked with special symbols, forwarding contents, along with the authentication user name and authentication information. Bringing the interest attributes into users' interest analysis so as to improve the accuracy of mining microblog users' interest keywords and topics. Topic quality assessment and perplexity evaluation were used to verify the effectiveness of the TIN-LDA model in mining the interest of microblog users.