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Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations.

ABSTRACT: The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to differentiate the size of the scope of articles' influences on the academic environment. Finally, the knowledge transfer capacity was discussed to investigate the support degree of articles on the follow-up research. Experimental results show that articles got more citations recently have a higher knowledge flow intensity. The articles have various impacts on the academic environment and have different supporting effects on the follow-up research, representing the differences in their knowledge diffusion and knowledge transfer capabilities. Compared with the single quantitative index of citation frequency, these knowledge flow patterns can carefully explore the citation value of articles. By integrating the three knowledge flow patterns to examine the total citation impact of articles, we found that the articles exhibit distinct value of citation impact even if they were published in the same field, in the same year, and with similar citation frequencies.

SUBMITTER: Wang M

PROVIDER: S-EPMC6872168 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations.

Wang Mingyang M Zhang Jiaqi J Jiao Shijia S Zhang Tianyu T

PloS one 20191121 11

The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to ...[more]

PMID: 31751395

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Project description:ImportanceWomen are less likely to be promoted and hold leadership positions in academic medicine. How often academic articles are cited is a key measure of scholarly impact and frequently assessed for professional advancement; however, it is unknown whether peer-reviewed articles written by men and women are cited differently.ObjectiveTo evaluate whether academic articles from high-impact medical journals written by men and women are cited differently.Design, setting, and participantsIn this cross-sectional study of all original research and commentary articles from 5 high-impact medical journals (Annals of Internal Medicine, British Medical Journal, JAMA, JAMA Internal Medicine, and The New England Journal of Medicine) from 2015 to 2018, the gender of the primary and senior authors of each article were identified using an online database, and the number of times each article has been cited was identified using Web of Science. The number of citations by primary and senior author gender were then compared. Data were analyzed from July 2020 to April 2021.ExposuresPrimary and senior authors' genders.Main outcomes and measuresNumber of citations per article.ResultsAmong 5554 articles, women wrote 1975 (35.6%) as primary author and 1273 of 4940 (25.8%) as senior author. Original research articles written by women as primary authors had fewer median (interquartile range) citations than articles written by men as primary authors (36 [17-82] citations vs 54 [22-141] citations; P < .001) and senior authors (37 [17-93] citations vs 51 [20-128] citations; P < .001). Articles written by women as both primary and senior authors had approximately half as many median (interquartile range) citations as those authored by men as both primary and senior authors (33 [15-68] citations vs 59 [23-149] citations; P < .001). Differences in citations remained in each year of the study and were less pronounced among commentary articles.Conclusions and relevanceIn this study, articles written by women in high-impact medical journals had fewer citations than those written by men, particularly when women wrote together as primary and senior authors. These differences may have important consequences for the professional success of women and achieving gender equity in academic medicine.

Project description:ImportanceRetraction is a tool that journals can use to deter research misconduct and alert their audience to erroneous content published in the journals. However, retracted articles may continue to damage science if they are still cited as legitimate articles.ObjectiveTo characterize patterns of postretraction citations, particularly in microRNA biomarker research, a field with one of the highest rates of retraction.Evidence reviewRetracted scientific articles on microRNAs were retrieved from PubMed, Web of Science, and Retraction Watch between database inception and July 17, 2021, according to preestablished search strategies. Control articles with characteristics in common with retracted articles (ie, published in the same journals in the same years and months and with the same number of authors) were matched and retrieved from PubMed. Citation metrics of retractions and control articles were collected from Web of Science. PubPeer was referenced to examine the public response or comments on included retractions. Data were analyzed from September 2021 through March 2023.FindingsA total of 10 461 articles were analyzed, with 887 retractions and 9574 articles as controls. Among retracted articles, which were published from 1999 to 2021, there were 756 articles (85.23%) written by researchers affiliated with Chinese institutions. Retracted articles were cited 6327 times after retraction. Of 792 retracted articles that were cited, 621 articles (78.41%) were cited at least once after retraction and 238 articles (30.05%) were cited more often after retraction than before retraction. Overall citations (comprising citations before and after retraction) and postretraction citations accumulated over time (eg, the median [IQR] number of postretraction citations was 1 [1-2] and 23 [9-44] citations at the first 6 and 66 months, respectively, between retraction and citation retrieval). A random sample of 87 retracted articles (9.81%) recorded 478 citations after retraction, with 208 citations (43.51%) in articles published 12 months or longer after retraction. Of these citing articles, 19 articles (3.97%) mentioned the retractions. Compared with the control group of 1620 nonretracted articles, no significant differences were found in overall number of citations or citations after retraction. Among 478 articles citing retracted articles, 414 articles were found on PubMed and had matched control articles; these articles had higher odds of being subsequently retracted than 7954 matched control articles (odds ratio, 6.57; 95% CI, 3.39-12.72).Conclusions and relevanceIn this study, retraction was not associated with a reduction in citations of retracted articles, but articles that cited retracted publications had higher odds of later retraction. These findings suggest that journals may need to implement mechanisms for detection of postretraction citations.

Project description:BackgroundMore than 400 articles with the title of 100 top-cited articles (Top100) have been published in PubMed. It is unknown whether their citations are fewer (or more) than those found in other bibliometric studies (Nontop100). After determining article themes using coword analysis, a temporal bubble graph (TBG) was used to verify the hypothesis that the Top100 had fewer citations than the Nontop100.MethodsUsing the Web of Science core collection, the top 50 most cited articles were compiled by Top100 and Nontop100, respectively, based on the research area of biomedicine and bibliometrics only. Coword analysis was used to extract themes. The study results were displayed using 6 different visualizations, including charts with bars, pyramids, forests, clusters, chords, and bubbles. Mean citations were compared between Top100 and Nontop100 using the bootstrapping method.ResultsThere were 18 citations in total for the 2 sets of the 50 most cited articles (range 1-134; 5 and 26.5 for Top100 and Nontop100, respectively). A significant difference in mean citations was observed between the 2 groups of Top100 and Nontop100 based on the bootstrapping method (3, 95% confidence interval: [1.18, 4.82]; 26.5, 95% confidence interval: [23.82, 29.18], P < .001). The 11 themes were clustered using coword analysis and applied to a TBG, which is composed of 4 dimensions: themes, years, citations and groups of articles. Among the 2 groups, the majority of articles were published in the journal of Medicine (Baltimore), with 9 and 7, respectively.ConclusionEleven themes were identified as a result of this study. In addition, it reveals distinct differences between the 2 groups of Top100 and Nontop100, with the former containing more recently published articles and the latter containing more citations for articles. Clinical and research clinicians and researchers can use bibliometric analysis to appraise published literature and to understand the scientific landmark using TBG in bibliometrics.

Project description:BACKGROUND:Social media coverage is increasingly used to spread the message of scientific publications. Traditionally, the scientific impact of an article is measured by the number of citations. At a journal level, this conventionally matures over a 2-year period, and it is challenging to gauge impact around the time of publication. OBJECTIVE:We, therefore, aimed to assess whether Web-based attention is associated with citations and to develop a predictive model that assigns relative importance to different elements of social media coverage: the #SoME_Impact score. METHODS:We included all original articles published in 2015 in a selection of the highest impact journals: The New England Journal of Medicine, The Lancet, the Journal of the American Medical Association, Nature, Cell, and Science. We first characterized the change in Altmetric score over time by taking a single month's sample of recently published articles from the same journals and gathered Altmetric data daily from the time of publication to create a mixed effects spline model. We then obtained the overall weighted Altmetric score for all articles from 2015, the unweighted data for each Altmetric component, and the 2-year citation count from Scopus for each of these articles from 2016 to 2017. We created a stepwise multivariable linear regression model to develop a #SoME_Score that was predictive of 2-year citations. The score was validated using a dataset of articles from the same journals published in 2016. RESULTS:In our unselected sample of 145 recently published articles, social media coverage appeared to plateau approximately 14 days after publication. A total of 3150 articles with a median citation count of 16 (IQR 5-33) and Altmetric score of 72 (IQR 28-169) were included for analysis. On multivariable regression, compared with articles in the lowest quantile of #SoME_Score, articles in the second, third, and upper quantiles had 0.81, 15.20, and 87.67 more citations, respectively. On the validation dataset, #SoME_Score model outperformed the Altmetric score (adjusted R2 0.19 vs 0.09; P<.001). Articles in the upper quantile of #SoME_Score were more than 5 times more likely to be among the upper quantile of those cites (odds ratio 5.61, 95% CI 4.70-6.73). CONCLUSIONS:Social media attention predicts citations and could be used as an early surrogate measure of scientific impact. Owing to the cross-sectional study design, we cannot determine whether correlation relates to causation.

Project description:Failed user authentication is a common event. Forgotten passwords and fingerprint non-recognition are the most common causes. Therefore, there is a need for efficient backup authentication methods, known as fallback authentication. However, fallback authentication methods suffer from different security and usability issues. This study aims to improve the security and usability of knowledge-based fallback authentication in the form of static security questions. The approach proposed in this study was designed considering different factors, such as question features, authentication mechanisms, and the use of tools to aid in composing memorable and secure answers. This study used a two-part experiment with 23 participants to evaluate the proposed approach based on security model testing. The results show that the proposed approach offered improved resistance to blind guess, focused guess, and observation guess attacks. While usability was clearly improved with questions that were based on recognition mechanisms, our results indicate that fallback authentication systems need a flexible level of security and avoidance of complexity in composing answers. In addition, our results indicate the effectiveness of using user behavioral details in the choice of topics for questions, where behavioral questions must have both high recall levels and resistance against guessing attacks. This work theoretically extends the knowledge of fallback authentication research by evaluating new security questions for fallback authentication considering replace of classical topics of security questions by introducing new topics of security questions based on user behavior and personal preferences. Also, this study applies methods of managing answers to security questions by encouraging users to compose answers based on free strict rules that inspire them to create strong and memorable answers based on their own rules. In addition, the findings of this study could support the deployment of knowledge-based authentication in fallback systems as a practical contribution to the user authentication field.

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Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations.

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Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations.

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