Based on the emerging concept of “Hybrid Intelligence,” this paper aims to explore a new model of human–computer interaction, and deeply research on its development and application of Intelligent Service in the big data environment. It systematically explores the related academic concepts of hybrid intelligence, and establishes its architecture model. The development of hybrid intelligence is faced with cognitive differences, system fragmentation, human–machine digital divide, and other issues. Strengthening the interaction between cognition and perception can be the key to break through the bottleneck. The intelligent service system based on the hybrid intelligent architecture takes knowledge fusion as the core, and “cloud intelligent brain” is making it possible for the human–computer symbiosis driven by hybrid intelligence. The proposed advanced human–computer interaction mode constructs a hybrid intelligent architecture model, enriches the concept system of human–machine hybrid intelligence, and provides a new landing scheme for intelligent services based on complex scenes in the big data environment.
The digital lifecycle encompasses definitive processes for data curation and management, long-term preservation, and dissemination, all of which are key building blocks in the development of a digital library. Maintaining a complete digital lifecycle workflow is vital to the preservation of digital cultural heritage and digital scholarship. This paper considers digital lifecycle programs for digital libraries, noting similarities between the digital and print lifecycles and referring to the example of the Digital Dunhuang project. Only through a systematic and sustainable digital lifecycle program can platforms for cross-disciplinary research and repositories for large aggregations of digital content be built. Moreover, advancing digital lifecycle development will ensure that knowledge and scholarship created in the digital age will have the same chances for survival that print-and-paper scholarship has had for centuries. It will also ensure that digital library users will have effective access to aggregated content across different domains and platforms.
This research provides a systematic analysis of 115 previous literatures on the use of academic social networking sites (ASNs) in scholarly communication. Previous research on the subject has mainly taken a disciplinary and user perspective. This research conceptualizes the use of ASNs in scholarly communication in the space between social interactions and the technologies themselves. Keyword analysis and scoping review approaches have been used to analyze the comprehensive literature in the field. The study found a geographic variation in what motivates academics to use ASNs. Scholar discovery and sharing are the primary driving factors identified in the literature. Four main themes within the research literature are proposed: motivation and uses, impact assessment, features and services, and scholarly big data. The study found that there has been an increase in scholarly big data research in recent years. The paper also discusses the key findings and concepts stated in each theme. This gives academics a better understanding of what ASNs can do and their weaknesses, and identifies gaps in the literature that are worth addressing in future investigations. We suggest that future studies may also extend the existing theoretical framework and epistemological approaches to better predict and clarify the socio-technical dimensions of ASNs use in scholarly communication. In addition, this study has implications for academic and research institutions, libraries and information literacy programs, and future studies on the topic.
With the development of mobile technologies, voice search is becoming increasingly important in our daily lives. By investigating the general usage of voice search and user perception about voice search systems, this research aims to understand users’ voice search behavior. We are particularly interested in how users perform voice search, their topics of interest, and their preference toward voice search. We elicit users’ opinions by asking them to fill out an online survey. Results indicated that participants liked voice search because it was convenient. However, voice search was used much less frequently than keyboard search. The success rate of voice search was low, and the participants usually gave up voice search or switched to keyboard search. They tended to perform voice search when they were driving or walking. Moreover, the participants mainly used voice search for simple tasks on mobile devices. The main reasons why participants disliked voice search are attributed to the system mistakes and the fact that they were unable to modify the queries.
An information search trail recommendation method based on the Markov chain model and case-based reasoning is proposed. A laboratory user experiment was designed to evaluate the proposed method. The experimental results demonstrated that novice searchers have a positive attitude toward the search trail recommendation and a willingness to use the recommendation. Importantly, this study found that the search trail recommendation could effectively improve novice searchers’ search performance. This finding is mainly reflected in the diversity of information sources and the integrity of the information content of the search results. The proposed search trail recommendation method extends the application scope of information recommendations and provides insights to improve the organization and management of online information resources.
This paper presents the findings of a study exploring the information practices of members of a religious organization. Its focus is the “Mahamevnawa Buddhist Monastery.” Particularly, this paper focuses on the study's findings in relation to participants’ information practices in constructing their understanding of “the Temple.” The study is informed by an information practices theoretical perspective, drawing on work from a variety of disciplines, including Castells’ space of flows, and Fisher's information grounds. Data was gathered from participant observation, interviews with both monks and devotees and email follow-ups, and analysis of the online presence of the temple through its website. Five social constructs for the temple appear frequently in the interviews: Virtual space; Physical/geographical place; Virtual space; Symbol; Process and practices; and Organization. Participants’ information practices are not only limited to spiritual purposes but also are linked to various social practices, activities, and interests. The study's findings suggest that constructions of place play a hitherto underexplored role in the multi-layered relationship between people and information.
It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.
Online companies face large user populations, making segmentation a daunting exercise. Demonstrating an approach that facilitates user segmentation, this research leverages product dissemination and product impact metrics with normalized Shannon entropy. Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the widest product dissemination and the least product impact using entropy-based measures, effectively capturing the engagement levels. We demonstrate that a small percentage (0.33% in our dataset) of products are so widely disseminated that they are non-discriminatory, and a large percentage of products (17.02%) are discriminatory but have so little dissemination that their impact is negligible. Our approach reduces the product dataset by 17.35% and the number of user segments by 8.18%. Implications are that organizations can isolate impactful products useful for user segmentation to enhance the user focus.