Juan C. Tocora, Abraham E. Gracia-Ramos, Diego A. Forero
In university hospitals, clinical care, teaching and research are the pillars of their missions. Scientometrics play a key role in the analysis of scientific productivity of researchers, laboratories or countries. However, there are no published articles about bibliometric studies of the scientific production of healthcare institutions in Latin America. To carry out a scientometric analysis of leading clinics and hospitals from five Latin American countries. We focused on five Latin American countries with the largest scientific production: Argentina, Brazil, Chile, Colombia and Mexico. We examined available information for international publications, citations, registered clinical trials, networks of collaborations and patent applications. The institutions with the highest numbers of published articles are: Hospital de Clínicas de Porto Alegre (Brazil), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (Mexico), Instituto Nacional De Cardiología Ignacio Chávez (Mexico) and Hospital Italiano de Buenos Aires (Argentina). Highly cited articles, networks of collaborations and patents applications were also identified. Scientometric analysis of health research around the globe has been quite helpful, in terms of identification of priorities for funding and support. The higher scientific productivity for some of these Latin American institutions might be explained partially by their higher levels of collaborations with colleagues in institutions in high-income countries, which usually have larger funding. We provide several recommendations for strengthening clinical research in this world region.
{"title":"A Scientometric Analysis of Research Productivity in Clinics and Hospitals from Five Latin American Countries","authors":"Juan C. Tocora, Abraham E. Gracia-Ramos, Diego A. Forero","doi":"10.5530/jscires.13.1.9","DOIUrl":"https://doi.org/10.5530/jscires.13.1.9","url":null,"abstract":"In university hospitals, clinical care, teaching and research are the pillars of their missions. Scientometrics play a key role in the analysis of scientific productivity of researchers, laboratories or countries. However, there are no published articles about bibliometric studies of the scientific production of healthcare institutions in Latin America. To carry out a scientometric analysis of leading clinics and hospitals from five Latin American countries. We focused on five Latin American countries with the largest scientific production: Argentina, Brazil, Chile, Colombia and Mexico. We examined available information for international publications, citations, registered clinical trials, networks of collaborations and patent applications. The institutions with the highest numbers of published articles are: Hospital de Clínicas de Porto Alegre (Brazil), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (Mexico), Instituto Nacional De Cardiología Ignacio Chávez (Mexico) and Hospital Italiano de Buenos Aires (Argentina). Highly cited articles, networks of collaborations and patents applications were also identified. Scientometric analysis of health research around the globe has been quite helpful, in terms of identification of priorities for funding and support. The higher scientific productivity for some of these Latin American institutions might be explained partially by their higher levels of collaborations with colleagues in institutions in high-income countries, which usually have larger funding. We provide several recommendations for strengthening clinical research in this world region.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Rafiq, Mochamad Bruri Triyono, I. W. Djatmiko, Pipit Anggraeni
{"title":"Engineering Education’s Potential for Virtual Reality Research and Development from 2012-2022: A Bibliometric Study","authors":"A. Rafiq, Mochamad Bruri Triyono, I. W. Djatmiko, Pipit Anggraeni","doi":"10.5530/jscires.13.1.26","DOIUrl":"https://doi.org/10.5530/jscires.13.1.26","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Keyphrase-Based Literature Recommendation: Enhancing User Queries with Hybrid Co-citation and Co-occurrence Networks","authors":"Mayur Makwana, Rupa Mehta","doi":"10.5530/jscires.13.1.18","DOIUrl":"https://doi.org/10.5530/jscires.13.1.18","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Darlenis Herrera-Vallejera, Salvador Gorbea-Portal
Foresight methods have been used by governments to reduce the margin of error in decision-making, but there is no golden rule for foresight activity; rather, several methods are combined to support decision-making. This article proposes an index number to support Technology Foresight in the field of Pharmacology/Pharmacy. The index number was formed by the relationship between bibliometric and human resources variables. First, Principal Components Analysis was used to reduce the initial bibliometric variables proposed by literature. Finally, Data Envelopment Analysis was used to calculate the number of Decision-Making Units (DMU), which are the most prolific institutions in the study country. The study examined 12 DMUs with 2,744 human resources (100% with academic degree) and 1,515 with research category (55.2%) from these, 217 granted patents (17.1% cited patents) and 1,017 papers (92.3% cited papers) were obtained. A simple but robust index was obtained to support decision-making in Technology Foresight. The results obtained from DMUs affect the Technology Foresight Index due to some institutions with low levels of scientific and technological activity and others with many highly qualified personnel. Technology foresight should be performed periodically by governments to reduce uncertainty in the innovation process and to develop highly competitive technologies. In this sense, this index is reliable for decision-making in the field of pharmacology/ pharmaceuticals. It proposes a novel index relating bibliometric variables (output indicator) and human resources variables (input indicator) to foresee the scientific and technological development in the field of Pharmacology/Pharmacy at the national level. In addition, this study includes variables representing scientific (paper) and technological (patent) activity, as well as the impact of both at the international level.
{"title":"Technology Foresight Index to Support Science and Technology Policy-Making in the Field of Pharmacology/Pharmacy: A Scientometric Analysis","authors":"Darlenis Herrera-Vallejera, Salvador Gorbea-Portal","doi":"10.5530/jscires.13.1.12","DOIUrl":"https://doi.org/10.5530/jscires.13.1.12","url":null,"abstract":"Foresight methods have been used by governments to reduce the margin of error in decision-making, but there is no golden rule for foresight activity; rather, several methods are combined to support decision-making. This article proposes an index number to support Technology Foresight in the field of Pharmacology/Pharmacy. The index number was formed by the relationship between bibliometric and human resources variables. First, Principal Components Analysis was used to reduce the initial bibliometric variables proposed by literature. Finally, Data Envelopment Analysis was used to calculate the number of Decision-Making Units (DMU), which are the most prolific institutions in the study country. The study examined 12 DMUs with 2,744 human resources (100% with academic degree) and 1,515 with research category (55.2%) from these, 217 granted patents (17.1% cited patents) and 1,017 papers (92.3% cited papers) were obtained. A simple but robust index was obtained to support decision-making in Technology Foresight. The results obtained from DMUs affect the Technology Foresight Index due to some institutions with low levels of scientific and technological activity and others with many highly qualified personnel. Technology foresight should be performed periodically by governments to reduce uncertainty in the innovation process and to develop highly competitive technologies. In this sense, this index is reliable for decision-making in the field of pharmacology/ pharmaceuticals. It proposes a novel index relating bibliometric variables (output indicator) and human resources variables (input indicator) to foresee the scientific and technological development in the field of Pharmacology/Pharmacy at the national level. In addition, this study includes variables representing scientific (paper) and technological (patent) activity, as well as the impact of both at the international level.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autonomous Vehicles (AVs) have the potential to (re)shape the urban transportation network drastically. The significant investment by the automotive industry and leading technology companies in AVs has resulted in a substantial surge in the number of published documents in this domain. Therefore, this study aims to analyze the current state and trajectory of AVs research by conducting a comprehensive review of the available literature. The study employs scientometric methods to examine the scientific landscape of AVs and assess the position of Urban Transportation Planning (UTP) within this context. The analysis encompasses both a macro-level perspective and a meso-level focus on UTP, utilizing datasets of journal articles published up to January 2023. The study addresses various questions such as identifying the main research trends, evaluating the impact and influence of countries and sources, determining the collaboration level among different countries, and assessing the maturity of AVs domain in the field of UTP. To accomplish this, the study analyzes the conceptual, intellectual, and social landscapes of AVs from both a holistic and macro-level perspective, as well as from the UTP perspective in a meso-level. The findings highlight a significant disparity between attention on AVs’ UTP aspect and their technical advancement, emphasizing the need for more comprehensive research to fully comprehend the implications of AVs deployment from the UTP perspective. The comprehensive understanding of the literature gained from this study will enable scholars to identify research gaps
{"title":"Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planning","authors":"Mazdak Sadeghpour, Eda Beyazit","doi":"10.5530/jscires.13.1.3","DOIUrl":"https://doi.org/10.5530/jscires.13.1.3","url":null,"abstract":"Autonomous Vehicles (AVs) have the potential to (re)shape the urban transportation network drastically. The significant investment by the automotive industry and leading technology companies in AVs has resulted in a substantial surge in the number of published documents in this domain. Therefore, this study aims to analyze the current state and trajectory of AVs research by conducting a comprehensive review of the available literature. The study employs scientometric methods to examine the scientific landscape of AVs and assess the position of Urban Transportation Planning (UTP) within this context. The analysis encompasses both a macro-level perspective and a meso-level focus on UTP, utilizing datasets of journal articles published up to January 2023. The study addresses various questions such as identifying the main research trends, evaluating the impact and influence of countries and sources, determining the collaboration level among different countries, and assessing the maturity of AVs domain in the field of UTP. To accomplish this, the study analyzes the conceptual, intellectual, and social landscapes of AVs from both a holistic and macro-level perspective, as well as from the UTP perspective in a meso-level. The findings highlight a significant disparity between attention on AVs’ UTP aspect and their technical advancement, emphasizing the need for more comprehensive research to fully comprehend the implications of AVs deployment from the UTP perspective. The comprehensive understanding of the literature gained from this study will enable scholars to identify research gaps","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Global Literature on Higher Education: A Bibliometric Analysis of Top 15 Journals","authors":"Muammer Maral","doi":"10.5530/jscires.13.1.23","DOIUrl":"https://doi.org/10.5530/jscires.13.1.23","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.5530/jscires.12.3.054
Hanbin Mao, Jixin Chen
Research output can be evaluated with productivity and impact, which are quantified by the numbers of publications ( N ) and citations N c , respectively. The h -index ( H ) unifies both factors. However, as an extensive variable, it grows with quantity of research output and favors senior researchers over juniors. In this report, by analyzing the data of the world top 2% scientists ( n = 179,597) from an online database, we found that h -index follows power laws and proposes a different model from what Hirsch has originally proposed. We propose intensive indices ( Q N and Q C ) to measure quality research by comparing the actual h -index of a researcher with the power-law fitted h -indices from the top 2% scientists with the same numbers of publications and citations respectively. We further calculated a dynamic research quality ( Q 1 = Q N / Q C ) and a reduced index ( Q 2 =( Q N Q C ) 0.5 ) to evaluate research quality over time. We rationalized that the power law dependency of quality research is due to its heterogeneous production pathways that require extra effort with respect to “regular” research output. We found that research quality for the top 2% scientists is maximized with ~100 citations/paper and with about ~100 publications. A major advantage of these indices is that they are relative to the academic peers with similar accomplishments in publications and citations, and therefore, are independent of career stages. Since Q indices are positively correlated with H/N ratios, the research quality can also be quickly and conveniently estimated by the readily accessible values calculated using the equation H/ (N)^(2/3) or H/(Nc)^(1/2) .
研究成果可以用生产率和影响力来评估，生产率和影响力分别用出版物数量（N）和引用次数（N c）来量化。h 指数（H）将这两个因素统一起来。然而，作为一个广泛的变量，它随着研究成果数量的增加而增长，并有利于资深研究人员而非年轻研究人员。在本报告中，通过分析在线数据库中世界排名前 2% 的科学家（n = 179 597）的数据，我们发现 h 指数遵循幂律，并提出了与赫希最初提出的不同的模型。我们提出了密集指数（Q N 和 Q C），通过比较研究人员的实际 h 指数和前 2% 科学家的幂律拟合 h 指数（发表论文数和引用次数分别相同）来衡量研究质量。我们还计算了动态研究质量（Q 1 = Q N / Q C）和缩减指数（Q 2 =( Q N Q C ) 0.5），以评估随时间变化的研究质量。我们认为，高质量研究的幂律依赖性是由于其生产途径的异质性，与 "常规 "研究成果相比，需要付出额外的努力。我们发现，排名前 2% 的科学家的研究质量在约 100 次引用/篇论文和约 100 篇论文时达到最高水平。这些指数的一个主要优点是，它们是相对于在论文和引用方面取得类似成就的学术同行而言的，因此与职业阶段无关。由于 Q 指数与 H/N 比值呈正相关，因此也可以通过使用公式 H/ (N)^(2/3) 或 H/(Nc)^(1/2) 计算出的现成值，快速方便地估算出研究质量。
{"title":"Quality Research Follows the Power Law","authors":"Hanbin Mao, Jixin Chen","doi":"10.5530/jscires.12.3.054","DOIUrl":"https://doi.org/10.5530/jscires.12.3.054","url":null,"abstract":"Research output can be evaluated with productivity and impact, which are quantified by the numbers of publications ( N ) and citations N c , respectively. The h -index ( H ) unifies both factors. However, as an extensive variable, it grows with quantity of research output and favors senior researchers over juniors. In this report, by analyzing the data of the world top 2% scientists ( n = 179,597) from an online database, we found that h -index follows power laws and proposes a different model from what Hirsch has originally proposed. We propose intensive indices ( Q N and Q C ) to measure quality research by comparing the actual h -index of a researcher with the power-law fitted h -indices from the top 2% scientists with the same numbers of publications and citations respectively. We further calculated a dynamic research quality ( Q 1 = Q N / Q C ) and a reduced index ( Q 2 =( Q N Q C ) 0.5 ) to evaluate research quality over time. We rationalized that the power law dependency of quality research is due to its heterogeneous production pathways that require extra effort with respect to “regular” research output. We found that research quality for the top 2% scientists is maximized with ~100 citations/paper and with about ~100 publications. A major advantage of these indices is that they are relative to the academic peers with similar accomplishments in publications and citations, and therefore, are independent of career stages. Since Q indices are positively correlated with H/N ratios, the research quality can also be quickly and conveniently estimated by the readily accessible values calculated using the equation H/ (N)^(2/3) or H/(Nc)^(1/2) .","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139201689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.5530/jscires.12.3.052
Emmanuel Baffour Gyau, Kulena Sakuwuda, Ernest Asimeng
{"title":"A Comprehensive Bibliometric Analysis and Visualization of Publications on Environmental Innovation","authors":"Emmanuel Baffour Gyau, Kulena Sakuwuda, Ernest Asimeng","doi":"10.5530/jscires.12.3.052","DOIUrl":"https://doi.org/10.5530/jscires.12.3.052","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.5530/jscires.12.3.058
Arshia Ayoub, Raashida Amin, Zahid Ashraf Wani
A tide of new research metrics of social web analysis has drawn the attention of researchers from quite some time now. This new alternative metrics – referred to as the Altmetrics, to some degree, is considered to complement the traditional metric indicators especially the citation metrics. Citations reflect the impact of the research mostly from the academic sections while Altmetrics besides academics demonstrates the consumption of research from a wider community including practitioners, instructors and general public too. Since, both i.e., Altmetrics and citation count are employed to gauge the research impact, therefore, this study attempts to correlate the two in order to determine their extent of comparability and association. The Altmetric scores were compared with the citation counts for the articles associated with the field of Biological Sciences, Earth and Environmental Science, History and Archaeology and Studies in Human Society from the list of top 100 articles, provided by the aggregator - altmetric.com for time period 2014 – 2017. Besides, the harvested articles' altmetric scores were correlated with the SJR (SCImago Journal Rank) of the journals of the respective articles in which they were published. Finally, Spearman's correlation was calculated to gauge the association between the variables. The study found that among the four categories, Earth and Environmental science shows the most significant correlation between the citation count and Altmetric score while for the Altmetrics and SJR score in the said field, no such trend is visible. History and Archaeology also shows the strong correlation between the Altmetrics and citation scores with the exception of articles for the year-2016 and somewhat similar trend was noted for the Altmetrics and SJR score of the publications. Biological sciences show a weak correlation for both the pairs of variables while those pertaining to studies in human society mostly show negative association for both sets of variables. Thus, from analysis it can be deduced that, excluding the category of human society, the other three categories (i.e., Biological Sciences, Earth and Environmental science, History and Archaeology), mostly show positive correlation between the Altmetrics and citation score of publications and also, to some extent, for the Altmetrics and SJR score of the publications. The study would provide an insight in the association and degree of relation between the two-research metrics for their better usability and applicability.
{"title":"Exploring the Impact of Altmetrics in Relation to Citation Count and SCImago Journal Rank (SJR)","authors":"Arshia Ayoub, Raashida Amin, Zahid Ashraf Wani","doi":"10.5530/jscires.12.3.058","DOIUrl":"https://doi.org/10.5530/jscires.12.3.058","url":null,"abstract":"A tide of new research metrics of social web analysis has drawn the attention of researchers from quite some time now. This new alternative metrics – referred to as the Altmetrics, to some degree, is considered to complement the traditional metric indicators especially the citation metrics. Citations reflect the impact of the research mostly from the academic sections while Altmetrics besides academics demonstrates the consumption of research from a wider community including practitioners, instructors and general public too. Since, both i.e., Altmetrics and citation count are employed to gauge the research impact, therefore, this study attempts to correlate the two in order to determine their extent of comparability and association. The Altmetric scores were compared with the citation counts for the articles associated with the field of Biological Sciences, Earth and Environmental Science, History and Archaeology and Studies in Human Society from the list of top 100 articles, provided by the aggregator - altmetric.com for time period 2014 – 2017. Besides, the harvested articles' altmetric scores were correlated with the SJR (SCImago Journal Rank) of the journals of the respective articles in which they were published. Finally, Spearman's correlation was calculated to gauge the association between the variables. The study found that among the four categories, Earth and Environmental science shows the most significant correlation between the citation count and Altmetric score while for the Altmetrics and SJR score in the said field, no such trend is visible. History and Archaeology also shows the strong correlation between the Altmetrics and citation scores with the exception of articles for the year-2016 and somewhat similar trend was noted for the Altmetrics and SJR score of the publications. Biological sciences show a weak correlation for both the pairs of variables while those pertaining to studies in human society mostly show negative association for both sets of variables. Thus, from analysis it can be deduced that, excluding the category of human society, the other three categories (i.e., Biological Sciences, Earth and Environmental science, History and Archaeology), mostly show positive correlation between the Altmetrics and citation score of publications and also, to some extent, for the Altmetrics and SJR score of the publications. The study would provide an insight in the association and degree of relation between the two-research metrics for their better usability and applicability.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.5530/jscires.12.3.055
Pablo Dorta Gonzalez
Governments are increasingly pushing researchers to engage in activities with societal impact, emphasizing the need for research dissemination and engagement with the broader public. This study addresses this imperative by investigating the multifaceted factors that influence social media attention, particularly on Twitter, for scientific research. Using Altmetric data and employing multiple linear regression analysis, this paper explores the determinants of Twitter mentions for research outputs. The study shows that certain factors have a significant impact on the level of engagement. In particular, the presence of research in mainstream news emerges as the most influential factor, highlighting the power of media coverage in increasing research visibility. In addition, research topics that align with highly topical issues, such as the COVID-19 pandemic, also garner significant attention on Twitter. Conversely, the influence of expert recommendations and the consolidation of knowledge in the form of review articles have a relatively weaker impact on Twitter mentions. In addition, this study underscores that public policy references in reports and citations within Wikipedia have limited influence in driving social media attention. Interestingly, mentions in patent applications do not have a significant impact in this context. In conclusion, this study provides valuable insights into the dynamics of research dissemination in the digital age and sheds light on the nuanced factors that can enhance or diminish its societal impact on Twitter.
{"title":"Factors that Influence How Scientific Articles and Reviews are Mentioned on Twitter","authors":"Pablo Dorta Gonzalez","doi":"10.5530/jscires.12.3.055","DOIUrl":"https://doi.org/10.5530/jscires.12.3.055","url":null,"abstract":"Governments are increasingly pushing researchers to engage in activities with societal impact, emphasizing the need for research dissemination and engagement with the broader public. This study addresses this imperative by investigating the multifaceted factors that influence social media attention, particularly on Twitter, for scientific research. Using Altmetric data and employing multiple linear regression analysis, this paper explores the determinants of Twitter mentions for research outputs. The study shows that certain factors have a significant impact on the level of engagement. In particular, the presence of research in mainstream news emerges as the most influential factor, highlighting the power of media coverage in increasing research visibility. In addition, research topics that align with highly topical issues, such as the COVID-19 pandemic, also garner significant attention on Twitter. Conversely, the influence of expert recommendations and the consolidation of knowledge in the form of review articles have a relatively weaker impact on Twitter mentions. In addition, this study underscores that public policy references in reports and citations within Wikipedia have limited influence in driving social media attention. Interestingly, mentions in patent applications do not have a significant impact in this context. In conclusion, this study provides valuable insights into the dynamics of research dissemination in the digital age and sheds light on the nuanced factors that can enhance or diminish its societal impact on Twitter.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139205863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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