A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

PurposeHow to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and s of articles.Design/methodology/approachThe authors conduct a comparison study of topic modeling on full-text paper and corresponding to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.FindingsThe authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding is higher when more documents are analyzed.Originality/valueFirst, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Impact of the COVID-19 Pandemic on Remote Working and Coworking Spaces in Germany—Narrative Literature Analyses

The COVID-19 pandemic has been hit the whole German society and with that the way of working as well as the trend of coworking, as it happened similarly in other western societies. With information about governmental measurements, the world of work, mobility and transportation, people's behavior, companies' strategies, the real estate market, and changes in new working spaces from different sources this article creates a narration of immediate impacts, medium-term and long-run effects. Finally, this article aims to draw potential coming changes and further trends for coworking spaces. © 2023, The Author(s).

Teleworking and sustainable behaviour in the context of COVID-19: the case of Lithuania

COVID-19 played a significant role in the spread of telework worldwide, changing people's lives and behaviour. The paper aims to identify how teleworking affected the sustainable behaviour of employees during the COVID-19 pandemic. The research design applies a multi-method approach, combining systematic and comparative scientific literature analysis and a semi-structured interview. The authors of the paper present the theoretical conceptual model, which illustrates links between teleworking during the COVID-19 pandemic and the sustainable behaviour of employees. The results of empirical research revealed that teleworking during the COVID-19 pandemic changed employee behaviour in economic, environmental and social dimensions. Positive changes were identified due to reduced commuting and shopping;decreased costs for transport, food, clothing, and beauty services;better access to healthy and nutritious food;better opportunities for professional development. On the contrary, costs for home energy and household waste increased. Adverse effects on employees' physical and mental health have been identified due to teleworking and COVID-19. Despite the identified negative effects, employees would like to continue teleworking even after the pandemic. © 2023 Ramunė Čiarnienė et al., published by Sciendo.

EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension.

OBJECTIVE: To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-text RCT evidence in PubMed into the structured representation. MATERIALS AND METHODS: Our representation, EvidenceMap, consists of 3 levels of abstraction: Medical Evidence Entity, Proposition and Map, to represent the hierarchical structure of medical evidence composition. Randomly selected RCT abstracts were annotated following EvidenceMap based on the consensus of 2 independent annotators to train an NLP pipeline. Via a user study, we measured how the EvidenceMap improved evidence comprehension and analyzed its representative capacity by comparing the evidence annotation with EvidenceMap representation and without following any specific guidelines. RESULTS: Two corpora including 229 disease-agnostic and 80 COVID-19 RCT abstracts were annotated, yielding 12 725 entities and 1602 propositions. EvidenceMap saves users 51.9% of the time compared to reading raw-text abstracts. Most evidence elements identified during the freeform annotation were successfully represented by EvidenceMap, and users gave the enrollment, study design, and study Results sections mean 5-scale Likert ratings of 4.85, 4.70, and 4.20, respectively. The end-to-end evaluations of the pipeline show that the evidence proposition formulation achieves F1 scores of 0.84 and 0.86 in the adjusted random index score. CONCLUSIONS: EvidenceMap extends the participant, intervention, comparator, and outcome framework into 3 levels of abstraction for transforming free-text evidence from the clinical literature into a computable structure. It can be used as an interoperable format for better evidence retrieval and synthesis and an interpretable representation to efficiently comprehend RCT findings.

On the economic impacts of COVID-19: A text mining literature analysis.

The COVID-19 outbreak has affected everyday lives worldwide. As governments started to implement confinement and business closure measures, the economic impact was felt by entire societies immediately. The urgency of such a theme has led researchers to study the phenomenon. Accordingly, the purpose of this research is to provide the state of the art on relevant dimensions and hot topics of research to understand the economic impacts of COVID-19. In this survey, we conduct a text mining analysis of 301 articles published during 2020 which analyzed such economic impacts. By defining a set of relevant dimensions grounded on existing literature, we were able to extract a set of coherent topics that aggregate the collected articles, characterized by the predominance of a few sets of dimensions. We found that the impact on "financial markets" was widely studied, especially in relation to Asia. Next, we found a more diverse range of themes analyzed in Europe, from "government measures" to "macroeconomic variables." We also discovered that America has not received the same degree of attention, and "institutions," "Africa," or "other pandemics" were studied less. We anticipate that future research will proliferate focusing on several themes, from environmental issues to the effectiveness of government measures.

Study on the Discovery of Personal Information on the Network of People Diagnosed with COVID-19

The arbitrary disclosure of information of people diagnosed with COVID-19on the network will adversely affect personal privacy and even violate the privacy rights of individuals. Through the method of literature analysis and case analysis, the information of the confirmed patients of COVID-19 is studied on the network disclosure. The study found that information disclosure can be divided into disclosable information and non-disclosure information, and make different ways of dealing with sensitive information, sensitive information must be handled with care, personal information processing must take into account the balance between personal interests and public interests. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

COVID-19 Literature Mining and Analysis Research

By 2019 COVID-19, since the epidemic, the number of relevant documents exponentially level rise. Faced with a large amount of literature, this research provides convenience for exploring the connection between research topics and fields and quickly understanding relevant literature information. We pass on the data set after data cleansing using the LDA(Latent Dirichlet allocation) methods, and Berts and K-means modeling method extracting topic keywords. Use knowledge graph tools to output relevant visual graphics and systematically extract adequate information. Through text mining of biomedical research papers related to COVID-19, the improved model is used to analyze and make recommendations to respond to and prevent the COVID-19 pandemic. This research can support the rapid and in-depth analysis of a large number of relevant documents and can be used in future research to support real-time scientific disease research. © 2022 IEEE.

COVID-19-Related Studies of Nonprofit Management: A Critical Review and Research Agenda.

During crises such as the present coronavirus disease-19 (COVID-19) pandemic, nonprofits play a key role in ensuring support to improve the most vulnerable individuals' health, social, and economic conditions. One year into the COVID-19 pandemic, an extensive automated literature analysis was conducted of 154 academic articles on nonprofit management during the pandemic-all of which were published in 2020. This study sought to identify and systematize academics' contributions to knowledge about the crisis's impact on the nonprofit sector and to ascertain the most urgent directions for future research. The results provide policymakers, nonprofit practitioners, and scholars an overview of the themes addressed and highlight the important assistance academic researchers provide to nonprofits dealing with the COVID-19 pandemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s11266-021-00432-9.

Analyzing the vast coronavirus literature with CoronaCentral.

The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as "long COVID" and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.

SARS-CoV-2 Causes a Different Cytokine Response Compared to Other Cytokine Storm-Causing Respiratory Viruses in Severely Ill Patients.

Hyper-induction of pro-inflammatory cytokines, also known as a cytokine storm or cytokine release syndrome (CRS), is one of the key aspects of the currently ongoing SARS-CoV-2 pandemic. This process occurs when a large number of innate and adaptive immune cells activate and start producing pro-inflammatory cytokines, establishing an exacerbated feedback loop of inflammation. It is one of the factors contributing to the mortality observed with coronavirus 2019 (COVID-19) for a subgroup of patients. CRS is not unique to the SARS-CoV-2 infection; it was prevalent in most of the major human coronavirus and influenza A subtype outbreaks of the past two decades (H5N1, SARS-CoV, MERS-CoV, and H7N9). With a comprehensive literature search, we collected changing the cytokine levels from patients upon infection with the viral pathogens mentioned above. We analyzed published patient data to highlight the conserved and unique cytokine responses caused by these viruses. Our curation indicates that the cytokine response induced by SARS-CoV-2 is different compared to other CRS-causing respiratory viruses, as SARS-CoV-2 does not always induce specific cytokines like other coronaviruses or influenza do, such as IL-2, IL-10, IL-4, or IL-5. Comparing the collated cytokine responses caused by the analyzed viruses highlights a SARS-CoV-2-specific dysregulation of the type-I interferon (IFN) response and its downstream cytokine signatures. The map of responses gathered in this study could help specialists identify interventions that alleviate CRS in different diseases and evaluate whether they could be used in the COVID-19 cases.

The swift knowledge development path of COVID-19 research: the first 150 days.

Scholars all over the world have produced a large body of COVID-19 literature in an exceptionally short period after the outbreak of this rapidly-spreading virus. An analysis of the literature accumulated in the first 150 days hints that the rapid knowledge accumulation in its early-stage development was expedited through a wide variety of journal platforms, a sense and pressure of national urgency, and inspiration from journal editorials.

Clinical Studies on the Treatment of Novel Coronavirus Pneumonia With Traditional Chinese Medicine-A Literature Analysis.

OBJECTIVE: This study aims to analyze the current situation and characteristics of traditional Chinese medicine for treatment of novel coronavirus pneumonia, clarify its clinical advantages and provide a reference for clinical treatment. METHODS: Clinical randomized controlled trials, clinical control trials and case series research involving the use of Chinese medicine for novel coronavirus pneumonia treatment were selected from PubMed, Chinese Journal Service Platform of CNKI, VIP, and WanFang Data Knowledge Service Platform from the establishment of the library to 11:00 am on April 15, 2020. The published information, research design, intervention measures and research observation index were statistically analyzed. RESULTS: Twenty studies were included. The research methods were mainly clinical controlled trials. The observation indicators were mostly fever improvement time, cough improvement time, shortness of breath improvement time, chest CT and CRP examination. Maxing Ganshi (Ephedrae Herba, Armeniacae Semen Amarum, Glycyrrhizae Radix Et Rhizoma, and Gypsum Fibrosum) decoction was the core prescription. The most frequently used drugs were Glycyrrhizae Radix Et Rhizoma (Gancao), Ephedrae Herba (Mahuang), Armeniacae Semen Amarum (Kuxingren), Atractylodis Rhizoma (Cangzhu), and Scutellariae Radix (Huangqin). The most frequently used drug combination was Ephedrae Herba (Mahuang)-Armeniacae Semen Amarum (Kuxingren). The most frequently used Chinese patent medicine was Lianhua Qingwen capsule/granule. CONCLUSIONS: Traditional Chinese medicine has widely used for novel coronavirus pneumonia in China. It is worthy of global attention. Also, high-quality randomized controlled clinical trials on the effectiveness and safety of traditional Chinese medicine in the treatment of novel coronavirus pneumonia need to carry out.