Topic Maps Research Articles

A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge.

Overconfidence Bias in Investment Decisions: A Systematic Mapping of Literature and Future Research Topics

The current study uses a systematic literature review to summarize and highlight studies on overconfidence bias in investment decision-making. More specifically, the study synthesizes the overconfidence literature highlighting the year of publication, country of the published articles, research methods, data sources, prominent theories, statistical techniques, citation analysis of the popular articles and authors and future research topics. The authors study 111 documents indexed in Scopus and/or Web of Science databases to recognize research trends regarding overconfidence bias during the last 29 years (1995–2023). The results indicate that most (61.26%) selected studies are empirical. Likewise, secondary data-based articles dominate primary ones. Additionally, the resulting factors can be classified into four themes: the construct of overconfidence bias and investments; success: a cause of overconfidence; gender and overconfidence; and the consequences of overconfidence. To the authors’ best knowledge, this is a unique article in which research outcomes of essential aspects of overconfidence are skimmed systematically.

Pemetaan penelitian information retrieval system menggunakan VOSviewer

Information retrieval systems help filter information, so that the process of finding suitable information becomes fast and precise, but there are ways to find more effectively and efficiently with Information retrieval systems using bibliometric methods with the VOSviewer application. The research objectives are to map the trends of research results on the topic of information retrieval system in the library, to map the research results on the topic of information retrieval system in the library based on co-authorship, and to map the research results on the topic of information retrieval system in the library based on co-occurrence. The research method uses descriptive bibliometric analysis by using the Scopus scientific journal database. The results of research on the topic of information retrieval systems in libraries are fluctuating where the most research results occurred in 2008 with publications totaling 13 articles and the lowest publications were seen in 2010 and 2014 with only 1 article produced. The level of author productivity is at 13 authors who produce a total of 2 articles. Co-authorship analysis is useful to see the mapping of research topics through relationships or collaborations between authors. The number of authors analyzed is 278, of which there are only 13 authors who show relationships between other authors. Co-occurrence analysis is useful for displaying bibliometric networks between keywords in a visual form. There are 326 keywords taken and 18 keywords have an occurrence relationship. Based on the bibliometric analysis conducted, it can be seen the distribution of research topics related to information retrieval systems in libraries, including research keywords that are still rarely researched, such as metadata and user studies. This can be used as an opportunity for further research topics.

Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences

Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.

Scientometric, fuzzy NGT and DEMATEL analysis for determining sustainable business practices for entrepreneurial firms

PurposeThe purpose of this article is to (1) map the most important topics in the domain of sustainable business practices for entrepreneurial firms in emerging countries, (2) identify important terms in the various dimensions of sustainability in business and (3) identify the important practices and prioritize the practices.Design/methodology/approachThis study has adopted a unique methodology that combines state-of-art scientometric analysis with the fuzzy nominal group technique (NGT) and fuzzy decision-making trial and evaluation laboratory (DEMATEL). Results obtained from the co-occurrence analysis in scientometrics were further mapped through NGT to obtain the list of the most important topics in the domain. The factors affecting sustainable business practices obtained through topic mapping were analyzed through fuzzy DEMATEL to obtain the cause-and-effect relation of variables.FindingsThe scale of firms, leadership, uncertainty, gender, country/location, education and tourism were found to be the factors affecting the sustainable business practices of entrepreneurial firms. The sustainable business practices for entrepreneurial firms were (1) innovation, (2) resilience, (3) policy, (4) business ethics and virtue ethics, (5) business model, (6) upcycling and value creation, (7) collaboration and (8) triple bottom line.Practical implicationsPolicymakers in entrepreneurial firms, as well as other organizations, can implement the identified sustainable business practices to obtain optimum results and smooth functioning of the companies. The research framework obtained can be tested using exploratory methods.Originality/valueVery few researchers have used the technique of scientometric analysis to identify the sustainable business practices of entrepreneurial firms, and to the best of the knowledge of the authors, no earlier researcher has attempted to use the technique of topic mapping, fuzzy NGT and fuzzy DEMATEL in combination.

Оценка влияния факторов природных условий на накопление человеческого капитала в восточных регионах Российской империи конца XIX — начала XX в.

The article presents a methodology for quantitative analysis of the influence of the natural-geographical environment on the accumulation of human capital and its testing on the data from the Asian part of the Russian Empire in the late 19th — early 20th centuries. The territorial unit of analysis is an uezd or district as a set of urban and rural settlements, an internal part of larger administrative entities, namely provinces and regions. The regression analysis showed that geographical factors are statistically significantly associated with the formation of human capital in the territory of the Eastern part of late imperial Russia. The research work was carried out based on a wide range of statistical and cartographic sources, including data from the 1897 census, reference maps of the 1914 atlas, climatic atlases of the Russian Empire and others. The significance and quantitative estimates of various factors (climate, natural resources, share of the urban population, and others) of human capital accumulation depending on the sample are presented. The article shows in detail the progress of the work and its methodology, including the identification of significant factors of literacy disparity in the context of “urban/rural” and “male/female” population». Input data and results of regression analysis are provided in the corresponding tables. The processed data set was presented as a composition of cartographic materials. Thus, topical maps were prepared for the source data, in particular on natural resources and climate; statistical results on disparity were displayed in the form of cartograms.

The nexus of public administration, public policy, and nonprofit studies: An empirical mapping of research topics and knowledge integration

AbstractThe interdisciplinary fields of public administration (PA), public policy studies (PP), and nonprofit studies (nonprofit) all contribute to our understanding of public affairs, but the nature and extent of their knowledge integration are empirically unclear. The current study adapts Rafols and Meyer's framework for understanding interdisciplinary research integration and applies bibliometric and qualitative methods to analyze citation trends among PA, PP, and nonprofit journal articles published between 2009 and 2020. Our findings reveal that articles in PA and PP journals cite nonprofit journals at a low (but consistent) rate, while those in nonprofit journals cite their PA and PP counterparts more frequently. Using qualitative coding, we developed a taxonomy of 15 broad research categories at the nexus of the fields: three of these (collaboration, networks, and partnerships; public service provision; and financial management) were shown to integrate knowledge from PA/PP and nonprofits, while several others indicated the potential to do so.

The Application of Artificial Intelligence to Ophthalmology: A Bibliometric Study (2000-2021)

Artificial Intelligence (AI) is an advanced technology for the latest 20 years. Machine learning (ML) and deep learning (DL) are the major innovations for AI, which has been applied for multiple fields. This study utilizes methods of text mining and bibliometric analysis to explore applications of AI to ophthalmology. 179 related articles from Web of Science (WOS) and 96 papers from China National Knowledge Infrastructure (CNKI) are explored from 2000 to 2021. A descriptive analysis of major trends, journal releasing, topic mapping and quotation relationships is implemented in this paper. Leading authors, journals, institutions, nations and references in the related research are identified. Findings show that the application of AI technologies in ophthalmologic diagnosis with optical coherence tomography (OCT) fundus images is the most hot topic for this area’s studies, especially for diabetic retinopathy (DR), aged macular degeneration (AMD) and glaucoma. It is also be predicted as the core direction over the recent years. Besides, The USA, England and China is the most competitive countries in this scientific filed. Journals of Ophthalmology, Investigative Ophthalmology and Visual Science, Eye, Acta Ophthalmologica and Scientific Reports are the top five journal related to the research area. There is a significant difference between WOS and CNKI databases pertaining to the application of Artificial Intelligence (AI) to ophthalmology, especially for the historic development, topic mapping and discipline category. Finally, the potential academic value of interdisciplinary of “AI in Ophthalmology” and tradition Chinese medicine (TRM) is discussed, and suggestions for the future research is indicated.

Introducing Cambridge Prisms: Coastal Futures

Whilst there is little argument that coasts are on the frontline when it comes to the impacts of near-future global environmental change, is there a need for yet another coastal journal? Emphatically ‘yes’. There is an unfilled niche here for a forum that promotes and presents cross-disciplinary research, from fundamental science to impact-orientated approaches. We wish to see a journal that informs pathways away from unsustainable practices towards more socially just and equitable futures for the world’s coastlines and their communities. We set out below some of the questions that arise when articulating this pathway, using the high-level categories in the journal’s topic map as some of the stepping stones that will be encountered along the way.

Relay and higher-order thalamic nuclei show an intertwined functional association with cortical-networks

Almost all functional processing in the cortex strongly depends on thalamic interactions. However, in terms of functional interactions with the cerebral cortex, the human thalamus nuclei still partly constitute a terra incognita. Hence, for a deeper understanding of thalamic-cortical cooperation, it is essential to know how the different thalamic nuclei are associated with cortical networks. The present work examines network-specific connectivity and task-related topical mapping of cortical areas with the thalamus. The study finds that the relay and higher-order thalamic nuclei show an intertwined functional association with different cortical networks. In addition, the study indicates that relay-specific thalamic nuclei are not only involved with relay-specific behavior but also in higher-order functions. The study enriches our understanding of interactions between large-scale cortical networks and the thalamus, which may interest a broader audience in neuroscience and clinical research.

Plant-Based Innovations for the Transition to Sustainability: A Bibliometric and in-Depth Content Analysis.

Plants maintain the ecological equilibrium of the earth and stabilize the ecosystem. Today, traditional commodities and new value-added markets can be served simultaneously. There is significant biosource and bioprocess innovation for biobased industrial products. Furthermore, plant-based innovation is associated with the transition to sustainability. This study performed a bibliometric and in-depth content analysis to review plant-based innovations in the research field between 1995 and 2022. A set of 313 articles was identified from the Scopus and Web of Science databases. Different analytical scientometric tools (topic mapping and overlay visualization networks) were used to analyze 124 articles; the most influential countries, institutions, authors, journals and articles were identified. Through in-depth studies, based on the grounded theory approach, five leading research areas related to plant-based innovation were determined: (1) agricultural/environmental innovation, (2) plant-based food or feed innovation, (3) innovation within the medical/pharmaceutical research area, (4) technology-related innovation and (5) economic/business aspects of plant-based innovations. Future research directions include exploring less examined and new topics, such as the sustainability implications of incorporating various plant-based foods and Industry 4.0 in plant-based innovation, and linking and developing findings from different research areas.

THE PRACTICE OF POLITICAL IDENTITY: A SYSTEMATIC REVIEW

This study aims to conduct a bibliometric analysis of the theme of political identity. This topic has recently become a center of attention and the strengthening of right-wing populism, for example, has succeeded in giving victory to several leaders such as Donald Trump, Josh Bolzonaro, and Duterte. Using the Scopus database from 2016 through 2021, concept mapping and clustering related to the theme of political identity will be seen. Data was collected through searching the Scopus database with the keyword political identity which resulted in 520 related articles. The data analysis focused on cluster analysis, dominant topics, related themes, and mapping of political identity study topics, based on the number of articles visualized and analyzed using VOSviewer and WordStat 8 software. This research shows that the study of political identity has four dominant keywords that often appear, namely: Political Identity, identity, Politics, and Political Ideology. This means that these four topics are the main focus of attention written by most authors. This shows that the topic of politics is very closely related to its use as a basis for one’s politics and will become a big challenge for democracy in the future.

The Origins, Evolution, Current State, and Future of Green Products and Consumer Research: A Bibliometric Analysis

Green products are crucial for a sustainable future. Without a strong understanding of consumer intent toward green products and research gaps, translating the availability of green products into actual consumer and market acceptance is hampered. This article reviews the available literature on green products and their relationship to consumers through bibliometric analysis. We used VosViewer to globalize the topic mapping and Scimat for longitudinal analysis. The results show that the available literature can be divided into four clusters, and five periods representing four distinct eras can be defined. Published studies were found in only 15 of the 36 calendar years constituting the first era. The second era started a wave of increasing green product research. In the third era, the number of journals with publications related to green products peaked. After the diversification of the third era, the fourth era saw the consolidation of the main vectors of publication. Despite a slow start in 1974, the research on eco-friendly products has expanded significantly over the past decade. Nonetheless, one persistent weakness of the literature is that most studies use customer intent, not the purchase itself, as the dependent variable. Consequently, there is still enormous potential for further research.

Cross-Corpora Comparisons of Topics and Topic Trends

Abstract Textual data gained relevance as a novel source of information for applied economic research. When considering longer periods or international comparisons, often different text corpora have to be used and combined for the analysis. A methods pipeline is presented for identifying topics in different corpora, matching these topics across corpora and comparing the resulting time series of topic importance. The relative importance of topics over time in a text corpus is used as an additional indicator in econometric models and for forecasting as well as for identifying changing foci of economic studies. The methods pipeline is illustrated using scientific publications from Poland and Germany in English and German for the period 1984–2020. As methodological contributions, a novel tool for data based model selection, sBIC, is impelemented, and approaches for mapping of topics of different corpora (including different languages) are presented.

Toward a Demand-Driven, Collaborative Data Agenda for Adolescent Mental Health

PurposeExisting datasets and research in the field of adolescent mental health do not always meet the needs of practitioners, policymakers, and program implementers, particularly in the context of vulnerable populations. Here, we introduce a collaborative, demand-driven methodology for the development of a strategic adolescent mental health research agenda. Ultimately, this agenda aims to guide future data sharing and collection efforts that meet the most pressing data needs of key stakeholders. MethodsWe conducted a rapid literature search to summarize common themes in adolescent mental health research into a “topic map”. We then hosted two virtual workshops with a range of international experts to discuss the topic map and identify shared priorities for future collaboration and research. ResultsOur topic map identifies 10 major themes in adolescent mental health, organized into system-level, community-level, and individual-level categories. The engagement of cross-sectoral experts resulted in the validation of the mapping exercise, critical insights for refining the topic map, and a collaborative list of priorities for future research. DiscussionThis innovative agile methodology enables a focused deliberation with diverse stakeholders and can serve as the starting point for data generation and collaboration practices, both in the field of adolescent mental health and other topics.

Patterns and conversational structure of knowledge sharing on Ebola virus disease among healthcare practitioners: Observations from a pilot study

This pilot study was designed to determine the conversational structure on Ebola Virus Disease (EVD), pattern of knowledge sharing and knowledge sharing behaviour of healthcare practitioners (HCP) on Medscape online network. The study was guided by Conversation Theory, and deployed a retrospective document/content analysis research design. Ninety-two comments from healthcare practitioners including 16 EVD newsposts out of 37 EVD news were randomly extracted from the network between 2014 and 2018. Extracted data were analysed using NVivo and Microsoft Excel applications. An EVD knowledge taxonomy was developed from the content mapping of EVD topics posted on the network. Results from the pilot study revealed that news on EVD were mostly on EVD outbreak management at the peak period of EVD outbreak in 2014. Also, the HCPs mostly engaged in knowledge sharing on issues relating to EVD risk. Findings revealed that the entire (37) newsposts were of interest to HCPs and they responded to 16 newsposts. There were three active and influential HCPs on the network. The novelty of this study stems from its focus on conversation patterns of HCP on online knowledge sharing using the variables of Conversation theory to examine their knowledge sharing behaviours. The outcome of the study could serve as a model for other studies on other trending viral diseases such as Coronavirus, Lassa Fever, Monkey pox, etc. This study thus recommends replicate analysis in the main study with a larger data set with further findings using network analysis and other statistical tools to examine the conversational structure on EVD and pattern of knowledge sharing among the HCPs.

The new generation: Contemporary Chinese art in the diaspora

This Special Issue of Journal of Contemporary Chinese Art focuses on the social significance and political relevance of diaspora Chinese art in the contemporary era. Although artists and authors may hold different stances towards Chinese and diaspora identities, their works and discussions showcase the importance of identity and identity-inflected art in contemporary times; they also demonstrate the productivity of treating Chinese diaspora art as a valuable subject of study in researching contemporary Chinese art. This editorial essay outlines the social and scholarly contexts related to a new generation of contemporary Chinese diaspora art and artists; it also introduces the structure and content of the Special Issue. The text is arranged in the following order: it first clarifies key words such as ‘diaspora’ and ‘Chinese diaspora’ and introduces scholarly debates surrounding these terms; it then briefly maps the study of contemporary Chinese art in a transnational and diasporic context to articulate the significance and scholarly contribution of the current issue. The essay ends with a mapping of the key topics and themes covered in this issue – which have implications for the study of Chinese diaspora art overall – and a brief outline of the key content and argument of each article.

A value creation model from science-society interconnections: Archetypal analysis combining publications, survey and altmetric data.

The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing the distinctive topic areas that correspond to different archetypes. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.

T-BERTSum: Topic-Aware Text Summarization Based on BERT

In the era of social networks, the rapid growth of data mining in information retrieval and natural language processing makes automatic text summarization necessary. Currently, pretrained word embedding and sequence to sequence models can be effectively adapted in social network summarization to extract significant information with strong encoding capability. However, how to tackle the long text dependence and utilize the latent topic mapping has become an increasingly crucial challenge for these models. In this article, we propose a topic-aware extractive and abstractive summarization model named T-BERTSum, based on Bidirectional Encoder Representations from Transformers (BERTs). This is an improvement over previous models, in which the proposed approach can simultaneously infer topics and generate summarization from social texts. First, the encoded latent topic representation, through the neural topic model (NTM), is matched with the embedded representation of BERT, to guide the generation with the topic. Second, the long-term dependencies are learned through the transformer network to jointly explore topic inference and text summarization in an end-to-end manner. Third, the long short-term memory (LSTM) network layers are stacked on the extractive model to capture sequence timing information, and the effective information is further filtered on the abstractive model through a gated network. In addition, a two-stage extractive–abstractive model is constructed to share the information. Compared with the previous work, the proposed model T-BERTSum focuses on pretrained external knowledge and topic mining to capture more accurate contextual representations. Experimental results on the CNN/Daily mail and XSum datasets demonstrate that our proposed model achieves new state-of-the-art results while generating consistent topics compared with the most advanced method.

One-year in: COVID-19 research at the international level in CORD-19 data.

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters-two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.