Nowadays, multiple phenomena have promoted an impact on society, constituting in some cases, not only a contribution of benefits but also of risks. Among them, the fake news phenomenon is considered one of the most burning phenomena today due to the risk it poses to society. In view of this situation, the research community has carried out numerous studies that seek to address this issue from a multidisciplinary perspective. Based on this, the objective of this work was to analyze the productivity and, therefore, the impact of this topic in the research community. To this end, this work advocated a scientometric-type methodology, through scientometric laws, impact indicators, and scientific evolution of 640 publications of the web of science (WOS). The results showed the impact of the fake news discipline today, which is considered an emerging issue that is of interest to many knowledge disciplines around the world. Likewise, the results showed that the publications not only have a focus on analyzing the veracity or not of the news, but that it begins to vertebrate a new line of an investigation directed to the informational education and towards the prevention of the consumption of this type of news through the internet.

https://www.mdpi.com/2076-0760/9/5/73/pdf

The Impact of Term Fake News on the Scientific Community. Scientific Performance and Mapping in Web of Science

Planning Digital Transformation of Care in Rural Areas

As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators.

/pdf/research-practice-and-progress-of-models-and-algorithms-2a7tdb2u.pdf

Research Practice and Progress of Models and Algorithms Applied in Topic Identification and Prediction Based on the Analysis of CNKI

The study focused on using scientometric methods to analyze forensic medicine and toxicology literature. The selected period for the study was from 2013 to 2022, allowing for a recent and comprehensive field analysis. The study used bibliographic data from a PubMed database, a widely used biomedical literature database. The study aimed to provide a comprehensive picture of the production and dissemination of the literature in the field. To achieve this, the study analyzed various bibliometric indicators, including theme analysis, keyword evolvement, and collaboration map. The analysis of trending topics and future thematic directions in the field was also conducted to provide insight that could inform and stimulate the research interests of young researchers. This scholarly article explains the construction of author networks, the assessment of author productivity yearly, and the analysis of affiliation networks and organizations. The number of articles published by an institution is an essential indicator of research productivity and expertise in the particular field. The study's findings provided valuable insights into the trends and patterns in forensic medicine and toxicology.

Evaluating Advancements in Forensic Medicine and Toxicology Through a Scientometric Mapping

Objective The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques. Methods The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics. Results The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of “Social and Technology in COVID-19” at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively. Conclusion One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category “Covid-19 Proteins: Vaccine and Antibody Response,” Iranian publications have a common publishing and research trend with international ones.

https://journals.sagepub.com/doi/pdf/10.1177/20552076231185674

Text classification technique for discovering country-based publications from international COVID-19 publications

This research was carried out using the bibliometric method to thematically analyze the articles on IoT in the Web of Science with Hierarchical Agglomerative Clustering approach. First, the descriptors of the related articles published from 2002 to 2016 were extracted from WoS, by conducting a keyword search using the “Internet of Things” keyword. Data analysis and clustering were carried out in SPSS, UCINET, and PreMap. The analysis results revealed that the scientific literature published on IoT during the period had grown exponentially, with an approximately 48% growth rate in the last two years of the study period (i.e. 2015 and 2016). After analyzing the themes of the documents, the resulting concepts were classified into twelve clusters. The twelve main clusters included: Privacy and Security, Authentication and Identification, Computing, Standards and Protocols, IoT as a component, Big Data, Architecture, Applied New Techniques in IoT, Application, Connection and Communication Tools, Wireless Network Protocols, and Wireless Sensor Networks.

A Thematic Analysis of the Articles on the Internet of Things in the Web of Science With HAC Approach

https://doi.org/10.1016/j.imu.2022.101144

ParsBERT topic modeling of Persian scientific articles about COVID-19

Theme trends and knowledge structure on health communication: Bibliometric analysis in PubMed database

Purpose As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics “Molecular Modeling,” “Gene Expression,” “Biomarker,” “Coronavirus,” “Immunoinformatics,” “Cancer Bioinformatics,” and “Systems Biology.” Moreover, “Systems Biology” and “Coronavirus” had the largest and smallest clusters, respectively. Conclusion The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other.

https://downloads.hindawi.com/journals/bmri/2023/3728131.pdf

Fezzeh Ebrahimi

Papers

A Thematic Analysis of the Articles on the Internet of Things in the Web of Science With HAC Approach

ParsBERT topic modeling of Persian scientific articles about COVID-19

Theme trends and knowledge structure on health communication: Bibliometric analysis in PubMed database

Analysis of Persian Bioinformatics Research with Topic Modeling

Identifying effective criteria for author matching in bioinformatics