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Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

Location-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.

The industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.

The coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.

This study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.

An industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

The spin-off of Iridium, a global telecommunications system, represented a significant business risk for Motorola, as many talented Motorola executives joined the venture in the late 1990s. This bold technology gamble suffered from numerous marketing missteps, which led to Iridium's bankruptcy in August 1999.

The purpose of this study is to examine different types of primary healthcare utilization and its influencing factors among the elderly in Indonesia.

Data were obtained from the 5th Indonesian Family Life Survey, a longitudinal database of demography and health information using multistage stratified sampling of households. Older adults aged at least 60 years were sampled; proxy respondents and incomplete data were excluded from the study.

Most of the elderly preferred to visit nurse/midwives practitioner (NP), followed by the community health centers (CHC). Those who lived outside of the Java region were more likely to visit NP; moreover, those without formal education and lived in the rural area were more likely to use CHC. Education level, region and chronic conditions were significant predicting factors for almost all types of primary healthcare use.

Indonesia is moving towards an aged society in the coming decades. However, there are significant barriers to access almost all types of primary health care by the elderly in Indonesia. This shows the possibility of health care inequality for the elderly population. This study provides evidence of the various types of primary healthcare use by the elderly and its influencing factors. It hopes policymakers can use the data to develop an effective strategy to enhance the quality of primary healthcare services provides to the elderly population.

Indonesian nurses fulfill vital functions in the health system and are often the only human resource for health not only in remote and poor rural areas but also in urban areas. Nurses, as a part of primary care providers, require an adequately trained to contribute to the better primary care system.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

Podcasting is a new mobile technology application for language learning. Drawing upon the stimulus–organism–response model and the interest driven creator (IDC) theory from e-learning, this study aims to develop and test an integrative conceptual framework. This study investigates contextual and environmental stimuli effects (content richness [CR], self-directed learning [SDL] and situational interest [SI]) from a podcast English learning context on learners’ experience states (cognitive absorption [CA], pleasure [PL] and arousal [AR]) and their subsequent responses (continuance learning intention [CLI]).

Using 416 valid responses from five universities located in North Taiwan, data analysis is performed using a structural equation model.

The results show that most of the interest factor stimuli (CR, SDL and SI) have significant impacts on learners’ experiences (CA, PL and AR), which in turn affect their CLI.

The findings provide useful insights for English show podcasters and operators to invest in establishing learners’ interest factor and stimulating experiences to improve their CLI.

This paper contributes to a better understanding of students who use contextual factors of podcast English learning and how these factors influence their CLI via a framework of stimulus–organism–response and the IDC theory.

The purpose of this paper is to identify the papers published on urban education and analyze the characteristics of these papers to contribute to the urban education research over the recent years.

Researchers generated a comprehensive list of the articles published between 2010 and 2017 in the field of “urban education” by searching the Social Sciences Citation Index database, using the keywords of “urban” and “education.” As an additional criterion, all the articles published between 2010 and 2017 in the journals of Urban Education and Education and Urban Society were included.

There are a total of 2,123 publications that were checked and retrieved in the field of “urban education” between 2010 and 2017. Although the number of publications differs year by year, the sum of the citations received the increase from year to year on a regular basis. There are 35 articles that were cited more than h-index, as h-classics in this field for this period and top 10 of these h-classics are reported in this study. Urban Education and Education and Urban Society were revealed as the most prominent journals in the field of urban education. Also, the results show that the most cited articles, the most prolific authors and organizations, and top journals in this field are from the USA.

The scope of this study is not extensive enough, and the data obtained are not comprehensive enough. The survey was limited to the keyword of “urban education” in the fields of title, abstract, author keywords and keywords plus.

This bibliometric analysis contributes to the literature of urban education through a historical perspective. Results show that the “urban education” field attracts more attention of the researchers and the impacts of these publications are increasing from year to year. Also, there is a prevalence of the USA in the field of urban education.

The information presented in this paper provides insight into the development of urban education research over the recent years. The data obtained in this study can be used as a reference for future researchers in urban education.

The purpose of this study was to explore the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. With the rapid development of information technology, internet of things has become an important carrier of people’s “intelligent life”. The emergence of intelligent library will no longer be limited by space; it is affecting people’s lives and work imperceptibly. This new service mode was studied here, and the relationship between the service quality of intelligent library and users’ behavior was analyzed from the perspective of user acceptance and use behavior of intelligent library. Moreover, this study explores how to optimize the service quality to let users accept this technology and service mode and thus realize the original idea.

Through 800 questionnaires issued to the users in the Zhejiang Provincial AI Library, the authors obtained the study data. Among the received questionnaires, 676 copies are valid, and 124 responses are either incomplete or not answered, and so, the efficient rate is 84.5 per cent.

There is a significantly positive correlation between service innovation and service quality. There is a significantly positive correlation between service quality and behavioral intention. There is a significantly positive correlation between service innovation and behavioral intention.

From the point of view of innovative service, this paper analyzes the effect of innovative service mode of intelligent library on improving the service quality and a series of impacts on user behavior. This study confirms that intelligent library is a relatively new service innovation. Users’ curiosity and exploration will make them access some relevant information. As a result, a reasonably innovative service is an important factor in users’ acceptance behavior.

It is particularly important to study the learner cognition based on information technology and change of learning environment in e-learning. Therefore, based on the combination of knowledge management and e-learning, the purpose of this paper is to discuss the basic rules and existing problems of virtual learning community (VLC).

In this study, a set of principles and basic framework of VLC was proposed, which could effectively instruct the construction of knowledge and collective wisdom development of VLC. Meantime, this paper revealed and constructed the theoretical framework and learning model for facilitating the advance of collective intelligence, described the main characteristics of collaborative knowledge building and traced the trajectory of convergence of collective intelligence and verified them through case studies and field studies.

The author analyzed the characteristics of VLC assisting knowledge construction and traced the trajectory of convergence of collective intelligence. In this way, the learners not only construct the meaning of knowledge in the process of participation from the edge to the center, but also complete the transformation from a green hand to an old hand. As a result, the learners form a sense of belonging to the community and complete the identity construction as a member of the community in the process of co-constructing the cultural tradition and value orientation of the community.

The analysis of the framework is made relatively in general terms, and lacks of the concrete, practical analysis. Therefore, it is necessary to adopt the social network analysis method to carry out systematic research on the framework, so as to make the actual operation of the VLC more practical.

It is significant to adopt the construction of the research system and theoretical framework in building VLC so as to improve the learner’s abilities of knowledge building and make full use of the advantages of the VLC.

This paper revealed and constructed the theoretical framework and learning model for facilitating the advance of collective intelligence. Besides, it provides a model reference for the follow-up construction of a new collective learning model.