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Expressional anomie (e.g. obscene words) can hinder communications and even obstruct improvements of national literacy. Meanwhile, the borderless and rapid transmission of the internet has exacerbated the influences. Hence, the purpose of this paper is detecting online anomic expression automatically and analyzing dynamic evolution processes of expressional anomie, so as to reveal multidimensional status of expressional anomie.

This paper conducted expressional anomie analysis via fine-grained microblog mining. Specifically, anomic microblogs and their anomic types were identified via a supervised classification method. Then, the evolutions of expressional anomie were analyzed, and impacts of users’ characteristics on the evolution process were mined. Finally, expressional anomie characteristics and evolution trends were obtained.

Empirical results on microblogs indicate that more effective and diversified measures need to be used to address the current large-scale anomie in expression. Moreover, measures should be tailored to individuals and local conditions.

To the best of the authors’ knowledge, it is the first research to mine evolutions of expressional anomie automatically in social media. It may discover more continuous and universal rules of expressional anomie, so as to optimize the online expression environment.

An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media.

This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users.

The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5.

The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers.

This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.

The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results.

This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion.

The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression.

To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic.

This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time.

Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method.

To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.

This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.

The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.

The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.

This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.

With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.

In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.

Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.

This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

The purpose of this paper is to explain how individuals form microblogging habits and why they continue to use microblogs from the perspective of direction social networks.

Drawing on the social network theory and the social presence theory, the authors develop a theoretical framework to explain how individuals form microblogging habits and why they continue to use microblogs. To test the proposed model and examine its external validity, the authors collected data from two microblogs: Twitter and Sina Weibo.

Satisfaction and habit have a significant influence on microblogging continuance intention. Whereas, users’ microblogging habits are developed by two key factors – satisfaction and frequency of past behavior – that are further determined by social presence and social network centrality.

Larger sample size with diverse populations is highly recommended for future studies. In addition, exploring the distinct technical functionalities of microblogs when conceptualizing habit formation would be of benefit in future studies.

In this study, it was found that social presence increases both the satisfaction of users and the frequency of past use behavior. Hence, microblog designers should provide users with greater freedom to modify the form and content of their interface, and enable these modifications to be visible in real time to increase the interactivity of microblogs.

In contrast to past studies that have largely neglected the impacts of the directed social network structure, this study aims to focus on microblogging continuance intention from the directed social network perspective. The results from two independent data sets converge on the conclusion that users’ continuance intention to use is affected by both their conscious evaluations (i.e. satisfaction) and unconscious reactions (i.e. habit).

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.