Proposed Direct and Indirect Citation Weighting Methods Based on Citation Context Similarity

2020 ◽  
Author(s):  
Toluwase Asubiaro
Keyword(s):  
Semantic Similarity ◽  
Citation Count ◽  
Research Impact ◽  
Knowledge Flow ◽  
Citation Context ◽  
Context Similarity ◽  
Level Of Knowledge ◽  
Direct Citation ◽  
Phd Thesis ◽  
Citation Contexts

Hypothetically, if paper B has cited paper A, and paper C has cited paper B, but paper C has not cited paper A; citation count can neither communicate the probable influence of paper A on C nor weigh the influence of A in B. In this case, paper A receives a direct citation from paper B, while it receives an indirect citation from paper C. This PhD thesis proposes methods for weighting direct and indirect citations which are based on the semantic cita-tion context similarity. The direct citation weighting is based on the unique-ness of in-text citation contexts, where unique in-text citation contexts attract more weights. The indirect citations are weighted based on the knowledge flow between papers A and C, that is, the semantic similarity between the ci-tation context of paper B in paper A and citation context of paper C in paper B where level of knowledge flow depends on the semantic similarity. Bio-medical publications will be used while semantic similarity is calculated based on cosine similarity which is implemented using the Fasttext-based bi-osentvec word embedding models. The proposed methods have the potential of being useful in determining the research impact of articles, authors and in-stitutions. They can also be useful in sorting of documents retrieved from in-formation retrieval systems.

scholarly journals Disciplinary differences in altmetrics for social sciences

2017 ◽  
Vol 41 (2) ◽  
pp. 235-251 ◽  
Author(s):  
Tint Hla Hla Htoo ◽  
Jin-Cheon Na
Keyword(s):  
Social Science ◽  
Pearson Correlation ◽  
Citation Count ◽  
Research Impact ◽  
Sign Test ◽  
Steady Increase ◽  
Content Type ◽  
Future Potential ◽  
Citation Counts ◽  
Science Disciplines

Purpose The purpose of this paper is to contribute to the understanding of altmetrics in different disciplines of social science: first, by investigating the current richness and future potential of altmetrics in the selected social science disciplines and then by evaluating the validity of altmetrics as indicators of research impact in each discipline through correlation analysis. Design/methodology/approach This study uses three approaches to understand the current richness and future potential of ten altmetric measures in nine selected disciplines: first, investigate the distribution and trend of altmetric data; second, verify the relationship between citation rate and altmetric presence of the discipline using Pearson correlation; and third, perform word frequency analysis on tweets to understand different altmetric presence in different disciplines. In addition, this study uses Spearman and sign test to find the correlation between altmetrics and citation counts for the articles that receive altmetric mention(s) to test the validity of altmetrics as indicators of research impact. Findings There is a steady increase in the number of articles that receive altmetric mentions in all disciplines studied. In general, disciplines with higher citation rates have higher altmetric presence. At the same time, altmetrics are also an effective complement to citation in disciplines with low citation rates. There is a moderate correlation with Mendeley and significant but weak correlations with Tweets and CiteULike in seven disciplines. Altmetrics appear effective as a predictor of citation counts in seven out of nine disciplines studied. However, there is low presence and lack of correlation with citation count in business-finance and law disciplines. Originality/value This paper furthers the understanding of altmetrics in social science disciplines. It reveals the disciplines where altmetrics are most effective, potentially useful, and fairly applicable. In addition, it presents evidence that altmetrics are an effective complement to citation in disciplines with low citation rates.

scholarly journals Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study (Preprint)

2019 ◽  
Author(s):  
Gregg Murray ◽  
Rebecca Hellen ◽  
James Ralph ◽  
Siona Ni Raghallaigh
Keyword(s):  
Social Media ◽  
Citation Count ◽  
Research Impact ◽  
Online News ◽  
Positive Correlation ◽  
News Sources ◽  
Journal Citation Reports ◽  
Social Media Platforms ◽  
The Impact ◽  
Article Citation

BACKGROUND Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. OBJECTIVE We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. METHODS We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. RESULTS For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (<i>r<sub>s</sub></i>=.513, <i>P</i>&lt;.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (<i>r<sub>s</sub></i>=.257, <i>P</i>&lt;.001). CONCLUSIONS Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

scholarly journals Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Libin Yang ◽  
Zeqing Zhang ◽  
Xiaoyan Cai ◽  
Tao Dai
Keyword(s):  
Dimensional Space ◽  
Attention Mechanism ◽  
Text Segment ◽  
Semantic Difference ◽  
Citation Context ◽  
Other Information ◽  
Scientific Papers ◽  
Low Dimensional ◽  
Text Content ◽  
Citation Contexts

With a tremendous growth in the number of scientific papers, researchers have to spend too much time and struggle to find the appropriate papers they are looking for. Local citation recommendation that provides a list of references based on a text segment could alleviate the problem. Most existing local citation recommendation approaches concentrate on how to narrow the semantic difference between the scientific papers’ and citation context’s text content, completely neglecting other information. Inspired by the successful use of the encoder-decoder framework in machine translation, we develop an attention-based encoder-decoder (AED) model for local citation recommendation. The proposed AED model integrates venue information and author information in attention mechanism and learns relations between variable-length texts of the two text objects, i.e., citation contexts and scientific papers. Specifically, we first construct an encoder to represent a citation context as a vector in a low-dimensional space; after that, we construct an attention mechanism integrating venue information and author information and use RNN to construct a decoder, then we map the decoder’s output into a softmax layer, and score the scientific papers. Finally, we select papers which have high scores and generate a recommended reference paper list. We conduct experiments on the DBLP and ACL Anthology Network (AAN) datasets, and the results illustrate that the performance of the proposed approach is better than the other three state-of-the-art approaches.

scholarly journals Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study

10.2196/15643 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e15643
Author(s):  
Gregg Murray ◽  
Rebecca Hellen ◽  
James Ralph ◽  
Siona Ni Raghallaigh
Keyword(s):  
Social Media ◽  
Citation Count ◽  
Research Impact ◽  
Online News ◽  
Positive Correlation ◽  
News Sources ◽  
Journal Citation Reports ◽  
Social Media Platforms ◽  
The Impact ◽  
Article Citation

Background Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. Objective We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. Methods We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. Results For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (rs=.513, P<.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (rs=.257, P<.001). Conclusions Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

scholarly journals The online attention to orthodontic research: an Altmetric analysis of the orthodontic journals indexed in the journal citation reports from 2014 to 2018

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Garcovich ◽  
Angel Zhou Wu ◽  
Ana-Matilde Sanchez Sucar ◽  
Milagros Adobes Martin
Keyword(s):  
Study Design ◽  
Citation Count ◽  
Research Impact ◽  
Online Activity ◽  
Journal Citation Reports ◽  
Related Quality ◽  
Scientific Outputs ◽  
High Level ◽  
The Impact ◽  
Journal Citation

Abstract Background To describe the impact of research, beyond the limits of the academic environment, Altmetric, a new social and traditional media metric was proposed. The aims of this study were to analyze the online activity related to orthodontic research via Altmetric and to assess if a correlation exists among citations, Mendeley reader count, and the AAS (Altmetric Attention Score). Method The Dimensions App was searched for articles published in the orthodontic journals listed in the Journal Citation Reports (JCR) throughout the years 2014 to 2018. The articles with a positive AAS were collected and screened for data related to publication and authorship. The articles with an AAS higher than 5 were screened for research topic and study design. Citation counts were harvested from Web of Science (WOS) and Scopus. Results The best performing journals were Progress in Orthodontics and the European Journal of Orthodontics with a mean AAS per published item of 1.455 and 1.351, respectively and the most prevalent sources were Tweets and Facebook mentions. The most prevalent topic was Oral Health-Related Quality of Life (OHRQOL) and the study design was systematic reviews. The correlation between the AAS and the citations in both WOS and Scopus was poor (r = 0.1463 and r = 0.1508, p < .05). The correlation between citations count and Mendeley reader (r = 0.6879 and r = 0.697, p < .05) was moderate. Conclusions Few journals displayed a high level of web activity. Journals and editors should enhance online dissemination of the scientific outputs. The authors should report the impact of the findings to the general public in a convenient way to facilitate online dissemination but to avoid an opportunistic use of the research outputs. Despite the lack of correlation, a combination of the citation count and the AAS can give a more comprehensive assessment of research impact.

Authorship trend and content analysis

2019 ◽  
Vol 21 (1) ◽  
pp. 33-51
Author(s):  
Jyotshna Sahoo ◽  
Basudev Mohanty ◽  
Oshin Biswal ◽  
Nrusingh Kumar Dash ◽  
Jayanta Kumar Sahu
Keyword(s):  
Content Analysis ◽  
Information Science ◽  
Research Collaboration ◽  
Citation Count ◽  
Research Impact ◽  
Research Field ◽  
Impact Measurement ◽  
Data Set ◽  
Content Type ◽  
Prolific Authors

Purpose The purpose of this paper is to examine the classic characteristics of highly cited articles (HCAs) of top-ranked library and information science (LIS) journals and get acquainted with the high-quality works in specific areas of LIS for distinguishing what gets cited and who the prolific authors are. Design/methodology/approach The HCAs published across the top four LIS journals were downloaded, coded and a database was developed with basic metadata elements for analysis using bibliometric indicators. Lotka’s Inverse Square Law of Scientific Productivity was applied to assess the author’s productivity of HCA. The content analysis method was also used to find out the emerging areas of research that have sought high citations. Findings Inferences were drawn for the proposed five number of research questions pertaining to individual productivity, collaboration patterns country and institutional productivity, impactful areas of research. The Netherland found to be the potential player among all the affiliating countries of authors and Loet Leydesdorff tops the list among the prolific authors. It is observed that Lotka’s Classical Law also fits the HCA data set in LIS. “Research impact measurement and research collaboration,” “Social networking” and “Research metrics and citation-based studies” are found to be the emerging areas of LIS research. Practical implications Researchers may find a way what gets cited in specific areas of LIS literature and why along with who are the prolific authors. Originality/value This study is important from the perspective of the growing research field of the LIS discipline to identify the papers that have influenced others papers as per citation count, spot the active and more impactful topics in LIS research.

scholarly journals unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

2020 ◽  
Vol 125 (3) ◽  
pp. 3085-3108 ◽  
Author(s):  
Tarek Saier ◽  
Michael Färber
Keyword(s):  
Real World ◽  
Data Sets ◽  
Context Analysis ◽  
Data Set ◽  
Plain Text ◽  
Scientific Disciplines ◽  
Citation Context ◽  
Scholarly Data ◽  
Future Evaluation ◽  
Citation Contexts

AbstractIn recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers’ plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

scholarly journals The Use of Social Media to Increase the Impact of Health Research: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Marco Bardus ◽  
Rola El Rassi ◽  
Mohamad Chahrour ◽  
Elie W Akl ◽  
Abdul Sattar Raslan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Health Research ◽  
Citation Count ◽  
Positive Association ◽  
Research Impact ◽  
Media Impact ◽  
Impact Studies ◽  
Use Of Social Media ◽  
The Impact

BACKGROUND Academics in all disciplines increasingly use social media to share their publications on the internet, reaching out to different audiences. In the last few years, specific indicators of social media impact have been developed (eg, Altmetrics), to complement traditional bibliometric indicators (eg, citation count and h-index). In health research, it is unclear whether social media impact also translates into research impact. OBJECTIVE The primary aim of this study was to systematically review the literature on the impact of using social media on the dissemination of health research. The secondary aim was to assess the correlation between Altmetrics and traditional citation-based metrics. METHODS We conducted a systematic review to identify studies that evaluated the use of social media to disseminate research published in health-related journals. We specifically looked at studies that described experimental or correlational studies linking the use of social media with outcomes related to bibliometrics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using a predefined search strategy (International Prospective Register of Systematic Reviews: CRD42017057709). We conducted independent and duplicate study selection and data extraction. Given the heterogeneity of the included studies, we summarized the findings through a narrative synthesis. RESULTS Of a total of 18,624 retrieved citations, we included 51 studies: 7 (14%) <i>impact studies</i> (answering the primary aim) and 44 (86%) <i>correlational studies</i> (answering the secondary aim). Impact studies reported mixed results with several limitations, including the use of interventions of inappropriately low intensity and short duration. The majority of correlational studies suggested a positive association between traditional bibliometrics and social media metrics (eg, number of mentions) in health research. CONCLUSIONS We have identified suggestive yet inconclusive evidence on the impact of using social media to increase the number of citations in health research. Further studies with better design are needed to assess the causal link between social media impact and bibliometrics.

scholarly journals Management of University Research Publication: A Case Study of JUIT Publication Database (JPubDB)

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Shri Ram ◽  
Nitin Paliwal
Keyword(s):  
Research Output ◽  
Citation Count ◽  
Research Impact ◽  
Global Community ◽  
Research Publication ◽  
Standard Format ◽  
Free Mode ◽  
And Performance ◽  
The University ◽  
The Impact

Along with the teaching, publications and research output, national and international funding has become essential criterion for benchmarking and performance measurement of the university. To stand and compete with stakeholders, it is essential to carefully monitor the impact of university publication over global research. The role of the library becomes more important to take a lead in monitoring and management of the university publications. A library needs to gather, organize and maintain publication in a standard format and take appropriate measure to disseminate research with global community. Further, it is essential to assess the research impact of the publication through different methodologies such as bibliometrics or web metrics. The purpose of this paper is to develop a database of university publication with the acronym ‘JPubDB’ (JUIT Publication Database: available at http://juit.ac.in/jpubdb), in order to collect, analyze and organized at one place and market the research publication with global community. The provisions have been made to link each publication with each faculty profile and department in a standard citation style, assess the citation count through Google Scholar, sharing of publication through social networking tools, and if the full text of any publication is available, that can be downloadable in copyright free mode.

Unwrap citation count, Altmetric Attention Score and Mendeley readership status of highly cited articles in the top-tier LIS journals

2020 ◽  
Vol 69 (8/9) ◽  
pp. 653-664
Author(s):  
Yingqi Tang ◽  
Hungwei Tseng ◽  
Charlcie Vann
Keyword(s):  
Impact Factor ◽  
Information Science ◽  
Citation Count ◽  
Science Research ◽  
Research Impact ◽  
Impact Factors ◽  
Library And Information Science ◽  
Content Type ◽  
Arts And Humanities ◽  
The Impact

Purpose The purpose of the study is to use a multidimensional perspective on the analysis of scholarly articles published in the top-tier Library and Information Science (LIS) journals. The relationships between the impact factors (Altmetric attention score [AAS], citation count and Mendeley readership) were analyzed, and reader profiles were characterized and studied. Design/methodology/approach This paper examined citation count, AAS and Mendeley readership of the most cited articles published in the top-tier LIS journals – The Journal of Academic Librarianship, Government Information Quarterly and Library and Information Science Research. A total of 61 articles were analyzed. Data were recorded on an Excel spreadsheet and exported to the statistical software package SPSS 18.0 for Windows to perform the descriptive and correlation analysis. Findings This study suggests that Mendeley readership and AAS could be used as supplemental measurements for assessing the impact of a publication or author in the LIS. AAS and Mendeley readership are positively correlated with citation count, and the correlation between Mendeley readership and citation count was stronger than AAS and citation count. Librarians are dominant readers of the top-tier LIS journals, followed by social sciences, computer science and arts and humanities professions. Originality/value This study introduces two newly launched metrics for measuring the research impact factor and discusses how they correlated with citation count. Moreover, the study details the spectrum of Altmetric for discovering readership of LIS top-tier journals. To the best of authors’ knowledge, this is the first study that presents the spectrum of AAS and Mendeley readership of the most cited articles published in top-tier of LIS journals. The study reveals an alternative way of measuring LIS publication’s impact factor that enables researchers, librarians, administrators, publishers and other stakeholders in LIS to assess the influence of a publication from another angle.

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