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User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible.

The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance.

Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically.

The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.

Topic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.

SenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.

Experimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.

The originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.

Drawing upon the theory of information culture from information science and prior research on Chinese information practices. The purpose of this paper is to posit that instead of over-relying on Hofstede’s national cultural values, the IS field should develop theories on national information culture to bridge the theoretical gaps found in explaining the differences in individuals’ IT perceptions and behaviors across cultures.

To achieve the objectives of this study, two quantitative studies were conducted. The first study is exploratory in nature and focuses on the development and calibration of an instrument to measure the Chinese information culture (CIC). The second study takes a confirmatory approach to investigate the impact of the CIC on IT perceptions of manager.

The study develops and validates an instrument for measuring the CIC that comprises information source and format preference, information ownership and analysis style, and centralized decision-making and information control. The CIC is found to influence IT perceptions of managers including perceived performance improvement resulted from IT use, attitude toward using work technologies and attitude on the value of IT in general.

This study is one of the first studies to theorize national information culture to understand IT perceptions at the individual level. The development of the CIC measure provides the much needed methodological support for contextualizing cross-cultural research.

In recent years, the use of high performing materials, and application of additive manufacturing technology for industrial production has witnessed a steady rise and its expanse is only to increase in the future. “Selective laser melting (SLM) technique” for an exotic nickel-titanium (NiTi) shape memory alloy (SMA) is expected to a great facilitator to research in this area. The purpose of this paper is to put forth the research direction of NiTi shape memory alloy by selective laser melting.

This review also summaries and skims out the information on process equipment, adopted methodologies/strategies, effects of process parameters on important responses e.g. microstructure and comprehensive functional and mechanical properties of SLM-NiTi. In particular, the functional characteristics (i.e. shape memory effects and super-elasticity behavior), process analysis and application status are discussed.

Current progresses and challenges in fabricating NiTi-SMA of SLM technology are presented.

This review is a useful tool for professional and researchers with an interest in the field of SLM of NiTi-SMA.

This review provides a comprehensive review of the publications related to the SLM techniques of NiTi-SMA while highlighting current challenges and methods of solving them.

AutoDx Case narrates an exciting story of how organizations in one industry (buyers, suppliers, and suppliers of suppliers), who fiercely compete with each other in the marketplace, collaborated to develop an online platform which would bring in significant efficiency in the system and benefit all the players. The case shows that first attempt to develop such a platform failed in the late 90s. However, similar attempt, later on, was inching towards success. That shows the impact of timing of technology and Shared Beliefs. While Case A narrates the problems faced by a few managers and needs for autoDx, Case B details how the project became successful and was under the process of adoption by various organizations.

AutoDx Case narrates an exciting story of how organizations in one industry (buyers, suppliers, and suppliers of suppliers), who fiercely compete with each other in the marketplace, collaborated to develop an online platform which would bring in significant efficiency in the system and benefit all the players. The case shows that first attempt to develop such a platform failed in the late 90s. However, similar attempt, later on, was inching towards success. That shows the impact of timing of technology and Shared Beliefs. While Case A narrates the problems faced by a few managers and needs for autoDx, Case B details how the project became successful and was under the process of adoption by various organizations.

This paper according to the logic of the “digital access divide--digital capability divide--digital outcome divide” aims to systematically discuss the impact of the digital divide on individual happiness in China, accounting for the variations that exist across different groups, as well as the corresponding mechanisms.

This paper presents cross-sectional analyses of the relationship between the digital divide and individual happiness in China. The analyses are based on data from the Chinese General Social Survey 2017, which academic institutions run on the Chinese Mainland. This database contains information on respondents' Internet access, skills and consequences of use, which can measure the digital divide of Chinese individuals at three levels.

First, individual happiness declined when they experienced the digital access divide in China. For the digital capability divide, the lower the usage skills, the more individual happiness declined. When analyzing the digital outcome divide, the greater the negative consequences, the more individual happiness declined. Second, the impacts of digital access, capability and outcome divide vary according to age, gender, education degrees, hukou, region and sub-dimensions. Third, the digital access and capability divide reduce individuals' happiness by lowering their self-rated social and economic status, whereas the digital outcome divide reduce individual happiness by lowering their fairness perception and social trust.

The authors believe that this is the first study to examine the impact and its variations among different groups of the three-level digital divide on individual happiness, as well as its mechanisms.

The purpose of this paper is to investigate the dynamic characteristics of a spiral groove liquid film seal considering the effect of cavitation.

A mathematical model of a spiral groove liquid film seal was established based on the mass-conserving Jakobsson–Floberg–Olsson cavitation boundary condition. The film rupture and film reformation boundaries were assumed to be unchanged under infinitesimal perturbation conditions. Governing equations under steady and perturbed states were solved by the finite element method, and then the dynamic characteristics of the spiral groove liquid film seal were theoretically investigated considering the effect of cavitation.

The results indicate that dynamic coefficients considering cavitation are smaller than those neglecting cavitation. The difference value is consistent with the change in cavitation area. The liquid film seal does not suffer axial instability whether considering cavitation, but its angular instability is more likely to occur when cavitation is considered.

For liquid lubricated non-contacting mechanical seals, the dynamic characteristics considering cavitation are investigated. The results are expected to provide a theoretical basis for improving the design method of liquid film seals.

The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|R_D) is able to erase the negative influences of error images in R_D.

Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.