Social Tagging Research Articles

Tags’ Recommender to Classify Architectural Knowledge Applying Language Models

Agile global software engineering challenges architectural knowledge (AK) management since face-to-face interactions are preferred over comprehensive documentation, which causes AK loss over time. The AK condensation concept was proposed to reduce AK losing, using the AK shared through unstructured electronic media. A crucial part of this concept is a classification mechanism to ease AK recovery in the future. We developed a Slack complement as a classification mechanism based on social tagging, which recommends tags according to a chat/message topic, using natural language processing (NLP) techniques. We evaluated two tagging modes: NLP-assisted versus alphabetical auto-completion, in terms of correctness and time to select a tag. Fifty-two participants used the complement emulating an agile and global scenario and gave us their complement’s perceptions about usefulness, ease of use, and work integration. Messages tagged through NLP recommendations showed fewer semantic errors, and participants spent less time selecting a tag. They perceived the component as very usable, useful, and easy to be integrated into the daily work. These results indicated that a tag recommendation system is necessary to classify the shared AK accurately and quickly. We will improve the NLP techniques to evaluate AK condensation in a long-term test as future work.

MeSH versus BibSonomy: A Comparative Study of Index Terms and Social Tags in Nursing Literature

Present study attempts to examine similarities and differences between the expert generated subject headings and social tags. The study focuses on the comparing BibSonomy social tags and MeSH terms. For this purpose hundred articles in the domain of nursing were collected from BibSonomy as well as PuMed. The results show that 92.72% MeSH terms are not available in social tag vocabulary and almost 15% social tags are likely to be accepted by MeSH terms i.e., experts. Jaccard similarity coefficient shows that users and experts use variant terminology. Users use mostly title-based keywords while experts have used mostly topic-based terminologies. It is suggested that while social tags could improve document retrieval. They cannot substitute controlled vocabulary. OPACs must allow users to describe library documents through user-generated social tags along with expert (librarian) generated terms.

A Bibliometric Analysis of Moderna mRNA-1273 Vaccine for COVID-19

We performed the 1st bibliometric analysis of the Moderna mRNA-1273 vaccine (for COVID-19). On May 28, 2021, the data was retrieved from the Scopus database. In total, two hundred and three (n=203) documents are published about Moderna, majorly comprising of reviews (n=84) and articles (n=66). In all documents (reviews and articles only), 1110 authors have significantly contributed. The documents per author were 0.135, while authors per document were 7.4. The collaborative Index (CI) was 8.34. By Lotka's Law, we provided information about the frequency of authors. For example, 1021 authors were involved in one (n=1) publication. The total number of publications, h-index, m-index, g-index, and total citations for all authors are provided. The highest documents are published by the National Institutes of Health NIH (n-8) & Moderna Therapeutics (n=8 and the National Institute of Allergy and Infectious Diseases NIAID (n=7). Fifty-five (n=55) countries have significantly contributed to all publications. The highest documents are published by the United States (n=74), India (n=30), and the United Kingdom (n=16). By Biblioshiny, the co-authorship network is also presented. All documents are published in 118 different sources, majorly in Vaccines (n=6), Frontiers in Immunology (n=5), and New England Journal Of Medicine (n=4). We also provided the H-index, g-index, and m-index of all sources. The top ten (n-10) most cited documents are briefly discussed, while we provided a general overview of the publications by co-words analysis. On December 18, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the Moderna COVID-19 (mRNA-1273) vaccine.

Global Research Trends on the Use of Nanotechnology to Boost Meat Production: A Scientometric Analysis.

Meat production plays a vital socioeconomic role for sustainable development and for promoting food security in most countries. However, not much is known about research agendas done globally and the advancement of knowledge-generating networks in this area of study. The present study aims to reveal and analyze scientific research outputs on meat production linked with recent nanotechnology research work done till date. A compilation of research advancement and development within the sphere was realized through a scientometric study to comprehend the trend of research outputs, scientific impacts, authors' involvement, collaboration networks, and the advancement of knowledge gaps for future research endeavors on the current subject matter. Scholarly published articles were retrieved from the web of science (WOS) and Scopus databases from 1985 to 2020 and they were merged together using bibliometric package in R studio. All duplicated articles (438) from both data bases were excluded. A combination of terms (nano* AND (livestock* OR meat* OR beef* OR mutton* OR pork* OR chevon* OR chicken* OR turkey*)), and conversely analyzed for scientometric indices. A collection of 656 peer-reviewed, research articles were retrieved for the study period and authored by 2,133 researchers with a collaboration index of 3.31. The research outputs were highest in the year 2020 with total research outputs of 140 articles. The topmost three authors' keywords commonly used by authors were nanoparticles, meat, and chitosan with a respective frequency of 75, 62, and 57. China, Iran, and India ranked top in terms of meat production research outputs linked to nanotechnology and total citation with respective article productivity (total citations) of 160 (3,193), 111 (1,765), and 37 (552). Our findings revealed an increasing trend in research (with an annual growth rate of 25.18%) tending toward advancing meat production with the use of nanotechnology. Likewise, there is an increasing pointer to the fact that research work on nanotechnology and meat production has the prospect to influence positively, decision-making on research direction, and collaborations, hereby increasing the production of meat and its products in the future.

Authorship Behavior in the Articles Appeared in Library Philosophy and Practice

This article presents a study about the authorship behavior and the collaboration pattern for the articles that appeared in the journal Library Philosophy and Practice during 2011-2016. The Zipf’s law of word occurrence and Lotka’s law are tested for this research. Further, the collaboration index, degree of collaboration, average paper per author, average author per paper, national and international collaboration is investigated for the period of study.

Assessment of hearing impairment research outcomes in top 10 countries

There are five types of publications such as all open access, in which the maximum research published their research publications, followed by green, gold, bronze, and hybrid gold respectively. A total of 17923 publications received 263785 citations and the overall publications trends have been shown in an increasing pattern while citations were shown in decreasing pattern. The overall relative growth rate was shown in decreasing trend while doubling time has been shown in an increasing trend. Medicine was the most famous area in which the maximum records were published i.e. 12973, followed by Biochemistry, Genetics and Molecular Biology with 3520 records while Hearing Impairment was the most using keyword, followed by Human. The most popular source in the research was ‘The International Journal of Pediatric Otorhinolaryngology, followed by Hearing Research, while Harvard Medical School contributed a large number of publications and the National Institutes of Health was the most popular funding agency in Hearing Impairment research. Researchers preferred to publish their research results in article type's document with 76% of records, followed by a review with 12% of records. Lin, F. R. from Johns Hopkins School of Medicine, Baltimore, United States has contributed the maximum number of 114 publications in Hearing Impairment research with 48 h-index and their total citations were 10063. The maximum research papers were contributed by three authors, followed by four authors’ while the overall collaboration coefficient and modified collaboration coefficient have been shown in increasing trends. However, the collaborative index lies between 4.72 (beginning year) to 4.88 (ending year) and the trend of the collaborative index is shown the increasing year by year except for only one year i.e. 2018. In the globe, the most productive country is the United States in Hearing Impairment research, followed by the United Kingdom.

Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection

Valuable decisions and highly prioritized analysis now depend on applications such as facial biometrics, social media photo tagging, and human robots interactions. However, the ability to successfully deploy such applications is based on their efficiencies on tested use cases taking into consideration possible edge cases. Over the years, lots of generalized solutions have been implemented to mimic human emotions including sarcasm. However, factors such as geographical location or cultural difference have not been explored fully amidst its relevance in resolving ethical issues and improving conversational AI (Artificial Intelligence). In this paper, we seek to address the potential challenges in the usage of conversational AI within Black African society. We develop an emotion prediction model with accuracies ranging between 85% and 96%. Our model combines both speech and image data to detect the seven basic emotions with a focus on also identifying sarcasm. It uses 3-layers of the Convolutional Neural Network in addition to a new Audio-Frame Mean Expression (AFME) algorithm and focuses on model pre-processing and post-processing stages. In the end, our proposed solution contributes to maintaining the credibility of an emotion recognition system in conversational AIs.

Personalised resource recommendation method for collaborative tagging system based on machine learning

In order to overcome the low feasibility of traditional resource recommendation methods, this paper proposes a personalised resource recommendation method based on machine learning. Firstly, the user-based collaborative filtering algorithm is used to calculate user personalised similarity, and then content-based collaborative filtering algorithm is used to calculate resource content similarity through cosine similarity. Combined with user similarity and resource content similarity, a hybrid computing model of resource similarity is established, and personalised recommendation is realised through statistical machine learning. The experimental results show that: the F-measure value of the method can reach 0.97, the coverage rate is maintained above 50%, the popularity is above 0.8, and the MAE value is always the minimum, and its precision is always higher than that of the contrast method. It shows that the proposed method can effectively improve the precision and feasibility of personalised recommendation results.

Scientometric Analysis of Soil Pollution Research Publications from SCOPUS Database during 2011 - 2020

This study aims to analyse the soil pollution research publications from the Scopus database during 2011-2020 with a total number of 35,572 research publications. During the study period, a maximum number of 4,857 (19.88%) publications are contributed in the year 2020, the highest number of 89,951 citations is received 3,019 publications in the year 2011 and compound annual growth rate 1.61. The relative growth rate is 0.69 in the year 2012 and 0.15 in the year 2020. At the same time, doubling time is found that 1.00 in the year 2012 and 4.72 in the year 2020. The majority of the authors in the field are preferred to publish their research works in four authorship modes with 6,359 publications. During the study period, the average degree of collaboration is 0.96, the average collaborative coefficient (CC) is 0.72 ties and the average collaborative index value is 4.38 and the average modified CC value is 4.38. A maximum of 142 (14.68%) research publications are contributed by Ok Y-S, India, and his publications are received 11,106 (25.61%) citations, CPP is 78.21, h-index is 59, and RCI value is 1.74. This study identified that time series analysis will be expected in the soil pollution research publications in the year 2025 is around are equal to 4,657 and the year 2030 is around are equal to 5,207.

Bibliometric Analysis of Major Neurosurgical Publications 2011-2020, Part 2: Journal, Author, Yearly Publication Trends, and Citation Related Metrics.

Background:Scientometrics, a subfield of bibliometrics, examines scientific publications by using bibliometric methods. The aim of a scientometric study is to study the various citation-based metrics of scientific articles, such as parameters pertaining to authors (including institutions and country of origin), articles, journals, and other citation related metrics.Objective:In this second part of our scientometric analysis of the 6 major neurosurgical journals from 2011–2020, we study journal and author trends, yearly publication trends, and citation related metrics in Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, Operative Neurosurgery, and World Neurosurgery.Methods:We analyzed parameters, including article and journal metrics (total articles published per journal per year, breakdown of the Bradford’s law distribution of journals, and Lotka’s law, journal impact factors), author metrics (country of origin, collaborations), citation totals, and keyword counts.Results and Discussion:The highest number of published articles from all journals occurred in 2011, the lowest in 2016. World Neurosurgery published the most. The author collaboration index has declined since 2016 with a notable drop in 2020 when the COVID-19 pandemic began. Impact factors remained stable, except for Operative Neurosurgery, which experienced a steep decline in 2020, and World Neurosurgery, which experienced a mild decline in 2020. Canadian authors were the most likely to participate in multi-country collaborations.Conclusion:The most articles were published in Journal of Neurosurgery, followed by Neurosurgery, Spine, and World Neurosurgery.

Global research output in ‘pharmacovigilance’ during 2010–2020

The rapid spread of Covid-19 pandemic globally has thrust drugs safety into the spotlight and the public is now more aware of the role of healthcare professionals and health regulators. The present study aimed to measure the global research landscape on pharmacovigilance (PV) indexed in Scopus database for eleven years period spanning from 2010–2020. The study has sought to use quantitative and visualization technologies for data analysis and interpretation. The search strategy accumulated a total of 2052 global publications data on PV. The findings disclose that the global research productivity on PV registered 8.74% average growth rate (AGR) and 7.38% compound average growth rate (CAGR). The mean relative growth rate (RGR) and doubling time (DT) of PV global publications for the 11 years is 0.27 and 3.03, respectively. The average number of authors per paper (AAPP) is 1.52 and average productivity per author (PPA) is 0.68. The authorship patterns in PV research shows collaborative trend as most of the publications have been published by multiple authors (80.75%). The mean values of degree of collaboration (DC), collaboration index (CI), collaboration coefficient (CC) and modified collaboration coefficient (MCC) during the selected period of study are 0.79, 2.74, 0.72, and 0.73, respectively which highly significant and indicates the better authorship collaborations. France is the bellwether in PV related scientific research as produced the highest number of publications.

Highly cited articles in social sciences: an analytical study

Purpose This paper aims at analyzing the distinctive characteristics of highly cited articles (HCAs) in the domain of Social Sciences with respect to chronological growth pattern, productive journals, authorship pattern, prolific authors, top institutions and leading countries, network among institutions and top ranked keywords in social science research. Design/methodology/approach The required data has been retrieved from Scopus indexing database and further refined using various limits like document types, subject coverage and total citations, and finally, 839 articles were selected for detail analysis. A set of bibliometric indicators were used to make a quantitative analysis, whereas VOSviewer software tool was used to visualize the institutional network and keywords mapping of the HCAs. Findings This study revealed that highest number of HCAs (371) were published during the decade 2001–2010. Degree of collaboration, collaborative index and collaborative coefficient were observed to be 0.513, 1.98 and 0.988, respectively. The highly cited papers were emanated from 397 journals, contributed by 1,556 authors from 1,326 institutions placed in 46 countries. Social Science and Medicine was the most productive journal; J. Urry of Lancaster University, UK, was the most influential author; the USA, the UK and Canada are the torchbearers in social science research. The paper entitled “Five misunderstandings about case-study research,” authored by B. Flyvbjerg, published in 2006 in Qualitative Inquiry, received highest 4,730 citations. Originality/value The primary value of this paper lies in extending an understanding of the characteristics of HCAs in the domain of social sciences. It will provide an insight to the researchers to get acquainted with the most influential authors, journals, institutions, countries and major thrust areas of research in social sciences.

Measurement of Accounting Information Accessibility and Understandability based on Distance of Information-state Transition Theory

With the advent of the era of big data, the problem of information selection and decision efficiency becomes more and more important under the network environment. Accounting information is an important economic information resource, but it has been facing the dilemma between the usefulness and useable. Based on the theory of Distance of Information-state Transition, this paper describes several concepts related to information distance, discusses key issues such as target state information, state chain, transition probability, measurement rules and determine standards, analyzes the influence of information retrieval, social tagging, recommendation system and information navigation on information distance. Based on the measurement of accounting information acquisition and knowledge acquisition, this paper studies the optimization of information distance in the network environment, which provides theoretical support and practical reference for the realization of effective accounting information utilization and information architecture

Bibliometric Visualisation of Research Performance of Post COVID -19 and Mucormycosis: Where Do We Stand?

Purpose: The second wave of the covid-19 pandemic has impacted global healthcare tremendously and mucormycosis associated with coronavirus disease is one of the deadly fungi that hit India in April 2021. An increasing number of research papers are upcoming with mucormycosis associated with coronavirus research and this paper aims at performing a bibliometric visualisation of all the available research on post covid-19 and mucormycosis.
 Method: The Scopus database was selected and the search query (ALL (novel coronavirus 2019 OR coronavirus 2019 OR COVID 2019 OR COVID 19 OR nCOV OR SARSCoV2 OR COVID19) and (black fungus or white fungus or yellow fungus or mucormycosis) was developed on 25 May 2021 to retrieve all the bibliographic records on the domine of interest. VOSviewer software tool was used to constructing and visualising bibliometric networks to measure co-authors, countries, and institutions document citation, keyword metrics.
 Results: A total of 154 documents were retrieved in the search, these were authored by 3,806 authors and published in 133 sources (journals, books, etc.). USA, India, and UK ware contributed the highest papers. Journal of Fungi (4), Heliyon (3), International Journal of Molecular Sciences (3), and Phytotherapy Research (3) are the journals that published the highest papers. Author per document was 24.7; Documents per author were 0.0405 and collaboration index was marked 26.5 during the period.
 Conclusion: this bibliometric visualisation presents the qualitative and quantitative metrics for post-covid-19 and mucormycosis research and provides evidence that research in this domine is more in-depth than before. It is hoped that this well-directed research in different countries will provide new avenues for understanding diseases caused by mucormycosis associated with coronavirus.
 Keywords: Bibliometric Visualisation; Post-Covid-19; Mucormycosis; Annual Growth Rate; Research Performance; India

POLISH PUBLICATIONS ON JUJUTSU (1906-2020): BIBLIOMETRIC ANALYSIS

The aim was to perform a bibliometric analysis of Polish jujutsu publications focused on productivity, topics, and collaboration. Thus, the National Library of Poland, Polish Scientific Journals Database, Web of Science and Scopus were consulted. Results were filtered and exported to Excel 2016 for statistical analysis. 123 Polish jujutsu documents were published from 1906 to 2020. Life Sciences had the highest representation, while collaboration predominated during the last decade. Cynarski was the most productive author, while Wojdat achieved the highest collaboration index. Ido Movement for Culture was the main source for disseminating these publications. In conclusion, technical books written by single authors from a Life Sciences approach prevailed in Polish jujutsu publications during the 20th century, while scientific articles written in collaboration and also representing other approaches have prevailed in the 21st century.

Co-authorship network structure analysis

The analysis of networks of collaboration between scientists reveals features of academic communities that help in understanding the specifics of collaborative scientific work and identifying the notable researchers. In these networks, the set of nodes consists of authors and there exists a link between two authors if they have coauthored one or more papers. This article presents an analysis of the co-authorship network based on bibliometric data retrieved from the distributed economic database. Here we use the simple network model without taking into account the strength of collaborative ties. The data were analyzed using statistical techniques in order to get such parameters as the number of papers per author, the number of authors per paper, the average number of coauthors per author and collaboration indices. We show that the largest component occupies near 90 % of the network and the node degree distribution follows a power-law. The study of typical distances between nodes and the degree of clustering makes it possible to classify the network as a ‘small world’ network.

An analysis of supply chain collaboration index by using FAHP and SCCI: a case study of organic fertiliser company X, Indonesia

Supply chain collaboration is one of the most crucial variables of driving business success in organic fertiliser Companies, especially to maintain the continual flow from upstream to downstream. Therefore, understanding the level of collaboration factors is vital in sustaining the partnership as well as reducing any conflicts among stakeholders. This study aims to measure the depth of collaboration among Company X and its suppliers. The assessment of the supply chain collaboration index will perform Fuzzy Analytical Hierarchy Process (FAHP) to weight the collaboration behaviour factors and Supply Chain Collaboration Index (SCCI) to measure the depth level of collaboration. The collaboration behaviour factors examined in this study are including joint effort, collaboration values, sharing activities, adaptation, trust, power, stability, commitment, continuous improvement and coordination. Based on the computation process using SCCI, the collaboration index among company X and its supplier is 76.72 on a scale of 1-100. It implies that the collaboration is at a moderate level. Consequently, the company needs to recognise the low score factors and develop a strategy for improvement. Some aspects that deserve further attention are sharing activities, power, and stability. By enhancing the performance of these factors, the supply chain collaboration index can also be increased.

Why didn't you tag me?!: Social exclusion from Instagram posts hurts, especially those with a high need to belong

Humans are highly sensitive to ostracism experiences and thus, even very short occurrences of being excluded and ignored can threaten fundamental needs and lower mood. We investigated whether not being tagged causes similar negative responses as being excluded in real life. Using a multi-method approach, we show across five studies (total N = 1149) that not being tagged in a posted photo strongly threatens fundamental needs. This effect is moderated by individuals’ need to belong, such that individuals with a higher need to belong experience not being tagged as more aversive. Results replicate across vignette studies in which participants imagine not being tagged on Instagram (Studies 2 and 3) and across studies using an alleged group task paradigm that mimicked the psychological mechanism of not being tagged outside of Instagram (Studies 4a and 4b). All experimental studies were pre-registered and we freely share all materials, code and data. Extending ostracism effects to the social media phenomenon tagging, the present research bridges real-world and digital social interactions. The results add to theoretical knowledge on social media, ostracism, and digital well-being and have practical implications for social media app design, social media interventions and our everyday interactions that increasingly happen online.

More than 100 years of neglect: a bibliometric analysis of global research on noma (cancrum oris).

Research plays a significant role in quantifying and addressing the burden of disease, improving healthcare delivery and developing evidence-based policy for neglected tropical diseases (NTDs). Since noma is not a recognised NTD by the World Health Organization (WHO), research activity is hypothesised to be low. This study aims to understand patterns and trends of research conducted on noma, to understand the patterns of citations, highlight networks of collaboration and analyse stakeholders in noma research. The SCOPUS database was selected and searched. Data analysis was conducted using the bibliometrix package for R in RStudio. Areas of interest included the distribution of research output on noma by year and location, the academic impact of publication output, mapping the major academic community and conducting a thematic analysis of high-frequency keywords. A total of 208 documents were identified. The annual growth rate of publications is 4.3%. The average number of citations per publication was 11.0 (rounded from 10.98) and the average citations per year per publication was 0.57. The USA was the country with the highest percentage 10.6% (n=22) of corresponding authors, followed by Nigeria (6.7% [n=14]), the UK (6.3% [n=13]) and India (5.8% [n=12]). For those papers with multiple authors, there was an average of 3.22 co-authors per document. The collaboration index was 3.3. Upon plotting collaboration networks between authors, only 11 clusters were identified (frequency and intensity of collaboration). Mapping of trending topics showed a focus on skin grafting and transplantation within the literature. In order to progress towards eventual elimination of noma it is key that more research should be undertaken and more interest and funding should be dedicated to work on noma. Recognition of noma as an NTD by the WHO would be a first step in moving towards increased awareness.

A Web of Science-based Analysis of Global Research Trends on <i>Moringa oleifera</i> from 2010 – 2019

Since time immemorial, people in developing countries relied on herbal folklore to treat different human diseases because of their numerous advantages over conventional drugs. As a result, the applications of medicinal plants are increasing in both advanced and developing countries owing to the emergent popularity of herbal products. <em>Moringa oleifera</em> (<em>M. oleifera</em>) is one of the most promising floras that have received global attention due to its versatile uses in medicine, industries, agriculture, nutrients, and coagulant in water treatment. Despite the extensive literature on this plant, no document has been reported on the quantity of research that has been carried out on the plant over time and as a result, this present study aimed to in retrospect evaluate the global research outputs on <em>M. oleifera</em> from 2010 to 2019. An overall of 989 articles was retrieved from Science Citation Index-Expanded (SCI-Expanded) and a bibliometric package in RStudio and VOS viewer were explored for data analysis and visualization, respectively. The research outputs were observed to have increased relatively over the year, and the most relevant author, institution, country, and journal were Bergamasco R (n = 33), UniversidadeEstadual de Maringá (n = 199), India (n =145) Industrial Crops and Products (n = 29), respectively. Co-authors/documents were 5, while 3.79 for the collaboration index. Findings from this study reveal the research collaboration network among the researchers and trend topics on <em>M. oleifera</em> and we believe that this study would provide valuable information for researchers interested in joining the field in the future.