A critical analysis of COVID-19 research literature: Text mining approach

A text mining and network analysis of topics and trends in major nursing research journals

AIM

This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology.

DESIGN

Text mining and network analysis.

METHODS

Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses.

RESULTS

Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study

Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry. A total of 694,366 documents were retrieved. Filtering the articles using text mining yielded 1,554 oral health/dentistry papers. The list of articles was classified into five topics after applying a Latent Dirichlet Allocation (LDA) model. This classification was compared to the author's classification which yielded 17 categories. After a full-text review of articles in the category "Oral manifestations in patients with COVID-19", eight papers were selected to extract data. The most frequent oral manifestations were xerostomia (n = 405, 17.8%) and mouth pain or swelling (n = 289, 12.7%). These oral manifestations in patients with COVID-19 must be considered with other symptoms to diminish the risk of dentist-patient infection.

Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.

The deployment of social media by political authorities and health experts to enhance public information during the COVID-19 pandemic

Social media have increasingly been used by political bodies and experts to disseminate health information to the public. However, we still know little about how the communication of these actors on social media is received by other users and how it reflects trends in public trust. We examined social media dynamics in the communication of information by major actors (n = 188) involved in COVID-19 online discussions in Switzerland. These actors are scientists (experts), policymakers (government officials, cantonal executives, and other parties), and representatives of mass media. We found little correlation between Twitter features (other users' engagement and negativity in other users' replies) and the level of public trust found in representative opinion surveys. We used topic modelling in combination with correspondence analysis, and including additional variables for actor types and the period of the public debate further enabled us to detect salient episodes related to the pandemic on social media. In particular, we found that differing roles were played by the (health) experts and political authorities in terms of both topics and influence on the specific timing of the pandemic. The results of this study provide helpful conclusions for communication among political authorities, health experts, and the public.

Detecting Latent Topics and Trends in Global Publications on Brucellosis Disease Using Text Mining

Purpose

Brucellosis is widespread globally and one of the most important zoonotic diseases. Therefore, to fully comprehend the disease and discover ways of prevention and treatment, researchers have conducted some research in this field. Hence, this study will focus on the topic trend of scientific publications of brucellosis.

Methods

This study is an applied research using text mining techniques with an analytical approach. The statistical population of the present research is all global publications related to brucellosis. For data extraction, the Scopus citation database was used in the period from 1900 to 2020. The main keywords for search strategy design have been extracted from consultation with thematic specialists and using MESH. Python programming language has been applied to analyze data and implement text mining algorithms.

Results

According to results, eight main topics of “Prevention,” “Clinical symptoms,” “Diagnosis,” “Control,” “Treatment,” “Immunology,” “Structural Features,” and “Pathogenicity” have been identified for brucellosis publications. Moreover, the topics “Prevention” and “Pathogenicity” had the highest and lowest prevalence in the field of brucellosis over time, respectively.

Conclusion

This study has revealed the topics published in the global publications of brucellosis; the findings can be useful for research centers and universities in determining research priorities in the field of brucellosis.

Non-Structural Flood Management in European Rural Mountain Areas—Are Scientists Supporting Implementation?

Mountain areas are highly exposed to flood risks. The latter are increasing in the context of climate change, urbanization, and land use changes. Non-structural approaches such as nature-based solutions can provide opportunities to reduce the risks of such natural hazards and provide further ecological, social, and economic benefits. However, few non-structural flood mitigation measures are implemented in rural mountain areas so far. The objective of this paper is to investigate if the scientific boundaries limit the implementation of non-structural flood management in rural mountain areas. In the study, we statistically analyzed the knowledge about flood management through a systematic literature review and expert surveys, with a focus on European rural mountain areas. Both methods showed that scientific knowledge is available for decision makers and that nature-based solutions are efficient, cost-effective, multifunctional, and have potential for large-scale implementation.

Past, Present and Near Future: An Overview of Closed, Running and Planned Biomethanation Facilities in Europe

The power-to-methane technology is promising for long-term, high-capacity energy storage. Currently, there are two different industrial-scale methanation methods: the chemical one (based on the Sabatier reaction) and the biological one (using microorganisms for the conversion). The second method can be used not only to methanize the mixture of pure hydrogen and carbon dioxide but also to methanize the hydrogen and carbon dioxide content of low-quality gases, such as biogas or deponia gas, enriching them to natural gas quality; therefore, the applicability of biomethanation is very wide. In this paper, we present an overview of the existing and planned industrial-scale biomethanation facilities in Europe, as well as review the facilities closed in recent years after successful operation in the light of the scientific and socioeconomic context. To outline key directions for further developments, this paper interconnects biomethanation projects with the competitiveness of the energy sector in Europe for the first time in the literature. The results show that future projects should have an integrative view of electrolysis and biomethanation, as well as hydrogen storage and utilization with carbon capture and utilization (HSU&CCU) to increase sectoral competitiveness by enhanced decarbonization.

Hydrogen Economy Development Opportunities by Inter-Organizational Digital Knowledge Networks

Innovative power-to-X (P2X) technologies, as a set of emerging new solutions, could play a crucial role in creating sustainable, carbon-neutral economies, such as the hydrogen economy. These technologies, however, are generally not yet implemented on a commercial scale. This research focuses on how innovative, digital inter-organizational knowledge networks of industry representatives and universities could contribute to the commercial implementation of P2X technologies and increase the pace of sustainable hydrogen-based development. The findings of an extended case study with a hybrid (qualitative–quantitative) methodology and a five-year time horizon, suggest the need for a digital knowledge platform, where universities and industry representatives add and combine their knowledge. In contrast with expectations, however, the empirical results show that academia would, not only be capable of supporting the exploration of new solutions, but foster the exploitation of more mature technologies as well. Similarly, large energy companies could also drive exploratory activities, not only exploitative ones. The findings highlight the possible central role of the “system builder” actor, who integrates exploitative-explorative learning and facilitates the formation of a (digital) innovation ecosystem. By exceeding the dominant techno-economic and environmental aspects, this research contributes to the literature by highlighting the applicability of network-based innovation management theory for hydrogen economy research.